- Email: [email protected]
Load forecast on intelligent buildings based on temporary occupancy monitoring
Energy and Buildings 116 (2016) 512–521
Contents lists available at ScienceDirect
Energy and Buildings journal homepage: www.elsevier.com/locate/enbuild
Load forecast on intelligent buildings based on temporary occupancy monitoring Jose A. Oliveira-Lima a,∗ , Ramiro Morais a , J.F. Martins a , Adrian Florea b , Celson Lima c a b c
CTS/UNINOVA, Dep. Eng. Electrotécnica, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa (FCT/UNL), Portugal Lucian Blaga University of Sibiu, Sibiu, Romania UFOPA, Instituto de Engenharia e Geociências, Universidade Federal do Oeste do Pará, Santarém, Brazil
a r t i c l e
i n f o
Article history: Received 4 May 2015 Received in revised form 20 January 2016 Accepted 21 January 2016 Available online 22 January 2016 Keywords: Building occupancy Energy consumption forecasting Neural networks Parking lot occupancy Intelligent buildings
a b s t r a c t The modeling of energy consumption in buildings must consider occupancy as a relevant input, since it plays a very important role in the overall building’s energy consumption. Frequently, buildings lack of permanent occupancy monitoring solutions. However, they may include data sources that are correlated with real building occupancy. This study proposes a new methodology for energy consumption modeling, supported by these alternative data sources, such as the number of vehicles in a parking lot. The aim is to mitigate investment in permanent occupancy monitoring solutions. The proposed methodology makes use of short-term real occupancy monitoring for model fitting, to enable the development of occupancy and energy consumption models, based on these alternative data sources. © 2016 Elsevier B.V. All rights reserved.
1. Introduction Buildings are major contributors to world’s energy consumption and CO2 emissions. These infrastructures, which often use more energy than necessary, are still being commissioned every day, and millions of energy inefficient buildings will remain in the upcoming decades. Usually, large buildings are operated by Building Management Systems (BMS), which control several electrical devices spread around the power grid to optimize building energy consumption and user comfort. Often, these BMS are quite inefficient as they only account for energy consumption data or are time-based operated, neglecting several other important factors, such as the number of occupants inside a building (or occupancy profile). The advent of Intelligent Buildings (IB) and the so-called NetZero Energy Buildings (NZEB), with their purpose of producing as much energy as they consume, has set the building energy efficiency goal increasingly higher [1–3]. Intelligent Buildings must be able to learn dynamically, adapting themselves according to several changing factors. Among them we may consider the outside environment, occupancy level and energy consumption, among others [4–6]. The inhabitants’ behavior influences the overall building energy consumption in a high degree and this study relies on the idea that it is possible to predict the electrical load of a building
∗ Corresponding author. E-mail address: [email protected] (J.A. Oliveira-Lima). http://dx.doi.org/10.1016/j.enbuild.2016.01.028 0378-7788/© 2016 Elsevier B.V. All rights reserved.
by using the building occupancy level. The optimization of building energy consumption must, therefore, include occupancy monitoring. Forecast and accurate occupancy profiles are thus required. However, most buildings do not use dedicated systems/devices to monitor occupancy level, due to building age or budget constraints. Nevertheless, in some cases, they may contain other systems that hold data related to the building’s occupancy level. Such systems may provide enough data to estimate approximate occupancy profiles. Some studies estimate building occupancy level by monitoring specific electric loads, such as lighting, Heating, Ventilation and Air Conditioning (HVAC), or other office equipment [7]. Others resort to building IT systems, such as Wi-Fi access points, instant messaging applications, schedules and mobile devices [8]. Since alternative occupancy estimation methods do not have access to real occupancy data, they often share the same problem: lack of accuracy. This paper proposes a new methodology to enable the estimation of occupancy profiles supported by alternative data sources, improving the accuracy and feasibility of both the estimation of building occupancy profile and energy consumption. The proposed methodology combines long-term coverage data sets of alternative occupancy data sources and building energy consumption with temporary real occupancy data acquisition, in order to fit the profiles of the estimated building occupancy level. It combines these short-term coverage and long-term coverage data sets with machine learning algorithms, such as Artificial Neural Networks (ANNs), in order to create both building occupancy and energy consumption profiles. The number of vehicles in a parking lot, during
J.A. Oliveira-Lima et al. / Energy and Buildings 116 (2016) 512–521
a stated period of time, is used as an Alternative Data Profile (ADP), emphasizing its potential in order to be used as a load forecast parameter and as an alternative data source for building occupancy representation. This work makes two major contributions: (a) it allows to accurately model real occupancy by the means of using alternative data sources. Contrary to most studies, this work doesn’t model sensor data that is related with real occupancy, but actually models real building occupancy with alternative data sources; and (b) it uses occupancy estimation (and not real occupancy) to model building energy consumption. In the following sections, the information is organized as follows: in Section 2 we review the Related work in the field of building load and prediction tools applied to estimate it. Section 3 describes the several stages to apply the proposed methodology to improve the estimation of occupancy profiles supported by alternative data sources, whereas Section 4 presents the experimental setup in order to determine the occupancy level of the building and of the parking lot. Laborious simulation results are illustrated in Section 5 to validate the proposed methodology. Finally, Section 6 suggests directions for future work and concludes the paper. 2. Related work 2.1. Building occupancy pattern and forecast For more than 20 years, the research in artificial neural networks has proved their usefulness in pattern recognition and pattern classification (forecast being a classification problem). In this section we review some approaches that are related to our work through the tools, applicability domain, learning algorithm or the methods of collecting the data sets required for prediction. In [9] the authors analyzed neural prediction techniques used in ubiquitous computing application. They predict the next visited room based on the history of rooms, by a certain person who moves within an office building. Occupancy prediction involves home heating control and efficient management of energy. In [10] occupancy sensing and historical occupancy data are used to estimate the probability of future occupancy profile, allowing the home to be heated only when necessary. In [11] the authors implement a multi-modal sensor agent, nonintrusive and low-cost, that combines information such as motion detection, CO2 reading, sound level, ambient light, and door state sensing, in order to estimate accurately the number of occupants in each room using different machine learning techniques (MLP and Support Vector Machines). 2.2. Occupancy determination methods The occupancy profile of a building may be obtained directly or indirectly. Directly, when the entrance and exit events are accounted for, and indirectly, if the number of occupants is obtained or estimated from other factors that may represent this number. Martani determined the occupancy level according to the number of wireless connections for each instant, concluding that it reflects the occupancy profile, leading to an electric load variation of over 60% [12]. Liao and Barooah indicate other indirect ways of determining occupancy profile: video cameras with people counting software, optical tripwires and PIR (pyro electric infrared), motion sensors for particular areas or CO2 , just to name a few [13]. Ekwevugbe used multiple sensors for redundancy in order to obtain more accurate results [14]. This approach emphasizes the effectiveness and benefits of low cost instrumentation to monitor the occupancy
513
level. The inaccuracy of the sensors can lead to residual or negative values of occupancy level the end of each day. As a solution to minimize this factor and eliminate possible future errors, Hutchins has proposed a method to eliminate these propagation errors between days, by resetting the occupancy level to zero at the end of each day, and including a clause according to which the occupancy level cannot be inferior to zero, thus preventing negative, unrealistic values [15]. In [16] a learning algorithm was used to learn the behavior of an occupant in single person office based on motion sensor data. The learned model predicts the presence and absence of the occupant with 80–83% accuracy. The occupancy data has only two states: occupied and non-occupied, and the accuracy is calculated as a Mean Absolute Error (MAE), which, per definition, is always lower than the MSE for the same data analysis. In [17] an auto-regressive hidden Markov model (ARHMM), is investigated to estimate the number of occupants in a research laboratory in a building using a wireless sensor network, and compared with a simple hidden Markov model (HMM). The authors use the MAE to assess the models’ accuracy, reaching values of 25.2–76.2% accuracy for the HMM and 80.1–84% accuracy with the ARHMM. The ARHMM shows better accuracy when the occupancy changes frequently. The stated studies assess the performance of learning algorithms in modeling occupancy using different sensor types. The model accuracy is calculated by comparing real sensor data with simulated model data. However, none of these methods assess model accuracy in terms of real occupancy data and real occupancy estimation. This work makes a contribution concerning this goal, by proposing a methodology to model real occupancy with alternative data sources. It changes the modeling approach, since the accuracy of the model isn’t assessed with alternative data source data, but instead with real occupancy data.
2.3. Main factors affecting building load In order to perform an accurate forecast of a building’s electrical load, it is necessary to know which factors affect its performance. Many studies have been made on that subject [18–20]. Leung states that cooling is responsible for approximately 45% of a building’s electric load [7]. The four main factors that affect the cooling demand of a building are: its own characteristics (location, orientation and type of building); systems designed to provide better conditions to occupants (HVAC systems, lighting); outdoor environmental factors (weather conditions such as temperature, humidity and wind) and occupants’ activities (human behavior). This last factor, human occupancy, is often of random nature and very hard to predict. Windows opening, manual HVAC parameters adjustment or lighting systems are some of the random variables that also aggravate the forecast of the building load [18]. Besides their behavior, the number of occupants at a given moment also influences the building load. Al-Mumin concluded, through a study about building occupancy profile in Kuwait, that the occupants’ lifestyle influences the building annual load [21]. The work hours, implying workers being away from home during the day, leads to different occupancy profiles between residences and workplaces. Therefore, the day type (holiday, weekend, weekday) and the daytime have a huge influence on the building’s load and should be considered as deterministic variables in related studies. The methodology proposed in this work makes a contribution to building energy consumption estimation by enabling to use data sources that may be related with building occupancy, to estimate its impact in overall building energy consumption.
514
J.A. Oliveira-Lima et al. / Energy and Buildings 116 (2016) 512–521
3. Proposed approach This work exploits correlations and ratios between distinct but related time-series data sets, aiming to enable the prediction and estimation of both building occupancy and building energy consumption. The prediction of building energy consumption is achieved by using an ADP that is indirectly related to building energy consumption and, simultaneously, directly related to building occupancy. Also, there should be enough data to model long-term coverage time-series profiles of such ADP. Data monitoring plays an important part in this process, building data sets from the ground up and continuously updating them. Fig. 1. ROP/ADP time-varying ratio.
3.1. Stage 1: Occupancy and energy consumption modeling The first stage of the proposed approach consists in data acquisition at three different levels, namely: (i) real building occupancy is temporarily monitored to support the learning of the Real Occupancy Profile (ROP); (ii) the ADP is permanently monitored to permanently build and update a long-term coverage data set and to support correlational reasoning together with the ROP; and (iii) Building Energy Consumption (BEC) is also permanently monitored to continuously build and update a long-term coverage data set and to support correlational reasoning with the ADP. ROP and ADP are specified as follows:
Fig. 2. Modeling building occupancy using an ADP and a ROP/ADP model.
• ROP(t,D,H,is Holiday) – real building occupancy over time (nonlinear) and represents a positive integer number (of persons) measured inside a building at each hour for each day. • ADP(t,D,H,is Holiday) = number of vehicles over time (nonlinear function the number of cars at each hour for each day measured outside of building, in the parking lot) where: • t = the number of day starting from 1 to 56 (8 weeks meaning of short-term data set) • D = the day of the week (Monday to Sunday) • H = the hour of the day (from 0 to 23) • is Holiday = the binary variable which defines the day as a holiday (Saturday, Sunday or vacations, national or religious free days) or not (Monday to Friday). The proposed strategy relies on three different data sets: a shortterm coverage data set of the real number of building occupants over time; a long-term coverage data set that is directly related to building occupancy profile; and a long-term coverage data set covering the building energy consumption over time.
3.2. Stage 2: Correlating data The second stage consists in generating a nonlinear function for the ROP and ADP data sets and comparing them during the same period of time. This gives the time-varying ratio of ROP/ADP, as shown in Fig. 1. The ratio between the ROP and the ADP should provide the estimation for the nonlinear function of persons per unit of ADP. The result is a short-term coverage data set with a time-varying ratio ROP/ADP, based on real data. This is a short-term coverage profile that relates the building occupancy level with units of ADP, as follows: • ROP/ADP(t,D,H,is Holiday) – persons per car function (nonlinear) obtained by dividing the number of occupants by the number of cars in the parking lot at each hour for each day during the short-term.
Persons per Car ROP ROP(t, D, H, is Holiday) = ADP(t, D, H, is Holiday) ADP(t, D, H, is Holiday)
(1)
In this work, a short-term data set of the real occupancy of the building is obtained by developing and using a temporary occupancy monitoring module. This approach is intended to prove that only a short-term period of real occupancy monitoring is required to develop a model of the building energy consumption, based on a long-term ADP. The ADP was developed based on the occupancy of the parking lot of a university campus, and was used to model the energy consumption of one building that belongs to that campus. 3.3. Stage 3: Profiling and extrapolation The applicability of the ROP/ADP ratio can be extended to bigger data sets by using the insight provided by short-term coverage data analysis. Short-term coverage data sets based on real observations support the development of models that make use of long-term coverage data sets of ADP to improve the forecast ability on longterm coverage building occupation. The third stage consists in using machine learning techniques to enable the learning of ROP/ADP models, thus allowing the building occupancy forecast for longterm coverage data sets of ADP. In this study ANNs are used to generate these models. As shown in Fig. 2, the short-term coverage data set of ROP/ADP is used to train the ANN and the ROP/ADP profile is modeled. Relevant features of ROP/ADP profile are then categorized into mean based profiles (e.g. daily, weekend, holiday profiles) and multiplied by the same profiles of the ADP long-term coverage data set. The number of occupants inside the building for past, present and future ADP data can then be calculated by developing profiles in terms of number of persons per unit of ADP at each hour. The result is a long-term coverage data set of building occupancy level, based on a trained ROP/ADP model:
J.A. Oliveira-Lima et al. / Energy and Buildings 116 (2016) 512–521
515
4. The experimental setup 4.1. Determining the occupancy profile of the building
Fig. 3. Modeling of building electrical load.
• ROP/ADP estimated(t + t,D,H,is Holiday) – the estimative function of the persons per car during the long-term.
The ADP can be the result of a real data set or of a trained model. In case the ADP results from a trained model, the outcome consists of the estimated future building occupancy profile. Being able to predict the future ADP (using ANNs or other learning technique) it is possible to predict the future number of occupants inside the building. An ANN was used in order to develop a ROP/ADP model based on data sets of building and parking occupancy levels. The purpose is to obtain a profile that enables us to forecast the electrical load for other time frames, using the number of vehicles in the parking lot. The ANN was developed using MATLAB’s Neural Network Toolbox due to its ease of use and capability of providing good results. The designed ANN is a Multilayer Perceptron (MLP), a feedforward network having one-way connections from input to output layers that is widely used to solve pattern classification problems. The model to estimate the number of persons per car was obtained by using inputs such as the days of the week, the time of the day and holidays. The proposed approach is intended to be independent of the modeling algorithm. The ANN was used with the purpose of validating the proposed approach.
3.4. Stage 4 – Load forecast According to the premise of this study, the electrical load of a given building depends on the building occupancy profile over time. Thus, electrical load can be correlated with building occupancy level, and reasoning models and tools that use occupancy as input can provide load estimations and previsions. The fourth stage consists in using machine-learning methods to build up models, that learn load profiles based on the building occupancy profile obtained from the combination of both short-term coverage ROP and long-term coverage ADP, as shown in Fig. 3. Long-term coverage data sets of both the real BEC over time and the estimated building occupancy level are required. In this work a MLP ANN was used to model the building energy consumption. The estimation of the building occupancy was used as input, together with time-related variables. This occupancy estimation was obtained by using the parking lot occupancy as ADP. This approach improves the BEC model accuracy by relying on an input (occupancy estimation) that results from a model fitted with real occupancy data, instead of using directly an ADP (parking lot occupancy), with potentially increased error. Again, the proposed approach is independent from the learning algorithm used. The ANN was used for validation purposes only.
This proposed methodology was implemented and tested in the campus of the Electrical Engineering Department of the Faculty of Sciences and Technology, Nova University of Lisbon. The building is located inside the University campus, which holds several car entrances monitored by a security system. The entrance and exit of vehicles is registered and allocated to student/teacher/college/course. This comprehensive data set of parking lot occupancy over time was used as ADP in this study. Also, the building’s real occupancy profile was monitored by a portable module for occupancy monitoring, developed for this study and installed at the building’s entrance, in order to determine the building’s ROP. This module consists of two laser beams Wenta Electronic LM-102-B119 aligned with two light dependent resistors (LDR) whose voltage is measured by an Arduino Duemilanove microcontroller board, with an ATmega328 controller. When one of the beams is interrupted by the presence of a person in front of it, the LDR resistance increases, causing a voltage drop. This voltage drop indicates that a person is entering or exiting the building. To determine the direction of the event, two beams are used. According to which beam is firstly interrupted, the movement will show entry or exit. Both entrance and exit events are recorded by the Arduino and sent to a computer via serial connection. The structure of the information stored consists of: (i) date and time; (ii) the total number of occupants of the building for a given instant; (iii) the total entry and exit events since initialization; and (iv) the number of timeouts since the data was last saved. Each timeout represents an incomplete beam event processing and is triggered whenever one of the beams is interrupted and if the other one is not interrupted during a 4 second period of time. Similarly, to other PIR-based systems, this module isn’t noise free. To improve the system’s accuracy, avoiding negative occupancy values and error propagation, the lowest total number of occupants is limited to zero and reset every day at 5 a.m. Fig. 4 shows the average weekly value for eight weeks of data acquisition. As depicted by this plot, Thursday is the day with the highest occupancy level, while Monday, Tuesday and Friday have similar occupancy levels. Wednesday has around half the occupancy level of other weekdays and the weekends have very little occupancy levels. In the horizontal axis a small vertical line segment denoting each weekday marks noon. 4.2. Parking lot occupancy as ADP This study comprises the evaluation of using the number of vehicles over time as an ADP to the proposed load forecast strategy. Particularly, it uses the parking lot occupancy profile from the University campus as ADP to estimate the number of persons inside a building over time. The parking lot is shared with several buildings that exist in the university campus. Thus, the ratio between the building and the parking lot occupancy levels (ROP/ADP) varies. The ADP data set contains all the entry/exit events recorded for all the campus’ gates during nine months’ time. These events are relative to the students, professors and staff of the Electrical Engineering department. The charts of the same eight weeks of data of ROP and ADP are represented in Fig. 5. The person per car function was obtained by dividing the number of occupants by the number of cars in the park, for each sample. By examining the plot in Fig. 5, it is possible to relate the building occupancy level represented by the top graphic plot, with the parking lot represented in the middle. Although both functions do not overlap, they display similar behavior and slopes in the majority of samples. The reason why they do not overlap is due to a large
516
J.A. Oliveira-Lima et al. / Energy and Buildings 116 (2016) 512–521
Fig. 4. Weekly average building’s occupancy.
Fig. 5. Building occupancy (top) vs. parking lot occupancy (middle) vs. person per car (bottom).
J.A. Oliveira-Lima et al. / Energy and Buildings 116 (2016) 512–521
517
Fig. 6. Electrical load monitoring setup.
number of students from this department having classes in other buildings and, therefore, may be accounted for in the parking lot but not in the building. The number of person per car represented by the bottom graphic plot shows some unrealistic peaks, occurring usually at around 8 a.m. The most evident is in the 6th day of samples with a peak of 64 persons per car. These are caused by the arrival at the car park of the first car of the day, having some people already registered in the building; they arrived by another means of transport or are not associated with the department but still have activity in the building. The correlation between the number of vehicles over time and time-related parameters, such as day of week, hour of day, and type of day was assessed, in order to validate the potential of using a learnt model of parking lot occupancy as ADP to support forecasts of future building occupancy level. A nine months’ data set of the number of vehicles over time in the parking lot was used
Fig. 7. ANN1 schematic used to develop a general profile of person per car.
and the Pearson product-moment correlation coefficient was calculated in 0.842, which reveals that there is a considerable correlation between the number of vehicles and the above-mentioned timerelated parameters.
Fig. 8. Actual values of person per car (top) vs. forecast results (bottom).
518
J.A. Oliveira-Lima et al. / Energy and Buildings 116 (2016) 512–521
Fig. 9. Recorded occupancy (top) vs. forecasted occupancy with 6 weeks data (bottom).
4.3. Electrical load monitoring The HVAC binary variable and the historical electrical load were obtained by installing an electrical load monitoring system in the Low Voltage Distribution Board (LVDB) of the building, as shown in Fig. 6. This monitoring system was conceived by installing two Algodue UPT210 multi-function energy meters, one for the general circuit and the other for the HVAC circuit. The acquired data was sent to a local computer running a JAVA application developed for this purpose. 5. Results 5.1. Building occupancy patterns using parking lot data An ANN1 was used in order to develop a ROP/ADP model based on the eight-week data sets of building and parking occupancy levels. The ANN1 was designed with 4 inputs, 10 neurons in hidden layer and 1 linear neuron in the output layer. The activation function is sigmoidal. The training process is conducted with the well-known Levenberg–Marquardt backpropagation algorithm, used for fast-supervised learning in feedforward networks using the Mean Squared Error (MSE) and gradient descent method. The inputs for ANN1 are days of the week, the time of the day, the binary variable which defines the day as a holiday or not, and the person per car obtained previously by dividing the occupancy level
Table 1 Average MSE and correlations of the 5 simulations with 8 weeks data. Training
Validation
Testing
MSE
R
MSE
R
MSE
R
8.91E−02
7.87E−01
9.00E−02
7.83E−01
9.21E−02
7.81E−01
of the building by the parking lot occupancy profile. The output is the forecast profile of person per car for the evaluated period. The training process used 70% of samples for training, 15% for validation and also 15% for testing because it seems to provide the smaller mean square error performance. Each simulation was run five times and the results were averaged. The layout of the developed ANN is represented in Fig. 7. The top curve of Fig. 8 represents the actual values obtained by dividing the occupancy level by the number of cars, and the bottom curve represents the forecasts, which are the ANN outputs. The performance of the ANN is represented in Table 1. To evaluate the model, the MSE and correlation (R) were computed with Matlab. MSE expresses the difference between the real output values and those generated by the ANN. The correlation coefficient is calculated by Pearson’s correlation coefficient, measuring the correlation between the real output values and the ones generated by the ANN. Both obtained MSE and correlation values show that the learnt model is reasonably accurate.
J.A. Oliveira-Lima et al. / Energy and Buildings 116 (2016) 512–521
519
Table 2 Average MSE and correlations of the 5 simulations with 6 weeks data. Training
Validation
Testing
MSE
R
MSE
R
MSE
R
5.82E−02
8.53E−01
6.06E−02
8.49E−01
6.08E−02
8.46E−01
Fig. 10. Overall schematic for the ANNs used with its inputs/outputs.
These results are related to the data acquired during the evaluation period. In order to extend the forecast of the building occupancy level to nine months’ time, the learnt values were split into a week-based profile. In order to do that, the forecasted values were processed to calculate the hourly average person per car for each day of the week. This average was then multiplied by the nine months’ data of the parking lot occupancy profile, resulting in the forecast of the building occupancy level for nine months’ time. In order to demonstrate the validity of this forecast, another ANN was developed, with similar characteristics to the previous one, with only six weeks of data. This way, two weeks of actual data can be used to validate the model. The obtained results are represented in Fig. 9. The performance of this ANN is represented in Table 2. Both the MSE and the correlation values show improved results. We can see that the values of R are of above 0.781.
5.2. Building load pattern In the previous subsection, the building occupancy forecast was obtained for nine months’ time. In this subsection another ANN was developed to forecast the building’s electric load for the same period of time. The overall ANN schematic diagram is represented in Fig. 10. The left part of the schematic diagram corresponds to
the one used in the previous section to forecast the person per car during nine months’ time. In order to model the occupancy-based electric load of the building, an ANN2 was developed with the following inputs: day of the week, time of day, binary variable describing the day as a holiday, the HVAC binary variable indicating its operation and the historical electrical load (represented as real electric load). The output was the electrical load forecast for nine months’ time. The samples were obtained between the beginning of October and the end of June. From December 3rd to June 4th the HVAC system has never been activated, and during the remaining period it operated at random cycles of 30–48 min, followed by idle states of approximately one hour. Therefore, it was concluded that its activation did not depend on the building occupancy level or temperature. Five simulations were run with all the inputs from Fig. 10. The results were averaged and this average represents the building’s electric load forecast for nine months’ time. The results are presented in Fig. 11. The electric load was normalized between 1 and 0, 1 represents the maximum and 0 the minimum. The forecasted load (top plot) and the actual load (bottom plot) are almost coincident, near to zero error (5,43E−8), showing a very accurate prediction. Samples from 1150 to 4515 correspond to the period of time when the HVAC system was turned off. In order to demonstrate the accuracy of the forecasts, the MSE and
Fig. 11. Forecasted load with HVAC data (top) vs. actual load (bottom).
520
J.A. Oliveira-Lima et al. / Energy and Buildings 116 (2016) 512–521
Table 3 Average mse and correlat ions with 9 Months data. Training
Validation
Testing
MSE
R
MSE
R
MSE
R
4.65E−03
8.43E−01
5.70E−03
8.13E−01
5.74E−03
8.19E−01
correlation averages of the simulation with HVAC data are represented in Table 3. 6. Conclusion This study presents a new methodology to forecast long-term coverage building electrical loads using temporary building occupancy monitoring. This methodology focuses on learning models that make use of long-term coverage occupancy ADPs and electrical load data sets, together with short-term coverage ROPs. ADPs are supported by data sources that are highly correlated with building occupancy level. For this research a nine months’ data set of the number of vehicles over time in a shared parking lot was used as ADP. A ROP was obtained by determining the occupancy profile of the building for a short period of time through the use of a temporary device installed at its entrance. The number of vehicles in the parking lot during a stated period of time shows a very high correlation with the same time-related parameters that were used to model the ROP. An ANN model was developed and made to generate the ROP/ADP profile. This model proved to be accurate due to its high correlation and very low MSE, and so was used to represent, in an indirect manner, the occupancy level of the building. Comparing the model accuracy results with the results from other occupancy modeling studies that use alternative data sources [16] [17], we can conclude that the proposed approach is able to model building occupancy with very high accuracy. Studies [16] and [17] assess accuracy using MAE. The best accuracy results obtained with these approaches are of 0.84. In this work, although using MSE, which per definition is always higher than the MAE for the same data analysis, we were able to achieve MSE of 9.21E−02 for 8 weeks of real occupancy data. A second ANN was developed to model the building electrical load. This model is supported by the ROP/ADP model and by longterm coverage data sets of ADP and building electrical load during a period of time. This model proved to be reasonably accurate, with high average correlation and low average MSE, which indicates that the method studied here is valid and can produce accurate and reliable results. From these results, it is concluded that the proposed methodology is able to model occupancy using alternative data sources, and that the building’s parking lot occupancy level can be used as a parameter for electric load forecast regarding a building, as the electric load profile is identical to the actual one, the forecast error obtained is very low and the correlation very high. As a future work, this study will be conducted in other institutions with different ADPs and compared to this particular case. Also, applying a similar methodology on other energy efficient applications, such as the estimation of the energy consumption of electrical circuits from Intelligent Buildings is another suggestion for a future study. References [1] I. Sartori, A. Napolitano, K. Voss, Net zero energy buildings: a consistent definition framework, Energy Build. 48 (2012) 220–232. [2] International Energy Agency (IEA), Energy Technology Perspectives 2010: Scenarios and Strategies to 2050, International Energy Agency Publications, 2010. [3] M. Sechilariu, B. Wang, F. Locment, Building-integrated microgrid: advanced local energy management for forthcoming smart power grid communication, Energy Build. 59 (2013) 236–243.
[4] Z. Zhao, W.C. Lee, Y. Shin, K.-B. Song, An optimal power scheduling method for demand response in home energy management system, IEEE Trans. Smart Grid 4 (Sep (3)) (2013) 1391–1400. [5] D. He, W. Lin, N. Liu, R.G. Harley, T.G. Habetler, Incorporating non-intrusive load monitoring into building level demand response, IEEE Trans. Smart Grid 4 (Dec (4)) (2013) 1870–1877. [6] J.H. Yoon, R. Baldick, A. Novoselac, Dynamic demand response controller based on real-time retail price for residential buildings, IEEE Trans. Smart Grid 5 (Jan (1)) (2014) 121–129. [7] M. Leung, N.C. Tse, L. Lai, T. Chow, The use of occupancy space electrical power demand in building cooling load prediction, Energy Build. 55 (2012) 151–163. [8] S.K. Ghai, L.V. Thanayankizil, D.P. Seetharam, D. Chakraborty, Occupancy detection in commercial buildings using opportunistic context sources, in: IEEE International conference on pervasive computing and communications workshops. IEEE, Mar 2012, 2012, pp. 463–466. [9] L. Vintan, A. Gellert, J. Petzold, T. Ungerer, Person movement prediction using neural networks, in: First workshop on modeling and retrieval of context, 2004. [10] J. Scott, A. Bernheim Brush, J. Krumm, B. Meyers, M. Hazas, S. Hodges, N. Villar, PreHeat: controlling home heating using occupancy prediction, ser, UbiComp’11, ACM, 2011, pp. 281–290. [11] S. Mamidi, Y.-H. Chang, R. Maheswaran, Improving building energy efficiency with a network of sensing, learning and prediction agents, in: Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems vol. 1, ser. AAMAS, International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 2012, pp. 45–52 [Online] Available: http://dl.acm.org/citation.cfm?id=2343576.2343582. [12] C. Martani, D. Lee, P. Robinson, R. Britter, C. Ratti, Enernet: studying the dynamic relationship between building occupancy and energy consumption, Energy Build. 47 (2012) 584–591. [13] C. Liao, P. Barooah, An integrated approach to occupancy modeling and estimation in commercial buildings, 2010, pp. 3130–3135. [14] T. Ekwevugbe, N. Brown, D. Fan, A design model for building occupancy detection using sensor fusion, in: [6th] IEEE International conference on digital ecosystems and technologies (DEST). IEEE, Jun 2012, 2012, pp. 1–6. [15] J. Hutchins, A. Ihler, P. Smyth, Modeling count data from multiple sensors: a building occupancy model, in: 2nd IEEE International workshop on computational advances in multi-sensor adaptive processing. IEEE, Dec 2007, 2007, pp. 241–244. [16] A. Agnetis, G. de Pascale, P. Detti, A. Vicino, Load scheduling for household energy consumption optimization, IEEE Trans. Smart Grid 4 (Dec (4)) (2013) 2364–2373. [17] T. Yu, Modeling occupancy behavior for energy efficiency and occupants comfort management in intelligent buildings, in: 2010 Ninth International conference on machine learning and applications, 2010, pp. 726–731. [18] B. Ai, Z. Fan, R.X. Gao, Occupancy estimation for smart buildings by an auto-regressive hidden Markov model, in: 2014 American Control Conference, 2014, pp. 2234–2239. [19] A. Badri, Z. Ameli, A. Birjandi, Application of artificial neural networks and fuzzy logic methods for short term load forecasting, Energy Proc. 14 (2012) 1883–1888. [20] A. Marvuglia, A. Messineo, Using recurrent artificial neural networks to forecast household electricity consumption, Energy Proc. 14 (2012) 45–55. [21] A. Al-Mumin, O. Khattab, G. Sridhar, Occupants’ behavior and activity patterns influencing the energy consumption in the Kuwaiti residences, Energy Build. 35 (Jul (6)) (2003) 549–559. Jose A. Oliveira-Lima was born in Lisbon, Portugal, in 1984. He obtained his B.S. and M.Sc. degrees in Electrical Engineering and Computing from the Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa (FCT/UNL), Lisbon, in 2006 and 2010, respectively, and he is concluding his PhD in the same faculty. He is also a researcher in the Center for Technologies and Systems, UNINOVA and was involved in research activities such as the NEMO & CODED and PRODUTECH PSI projects. He has published more than 15 scientific articles in peer-reviewed and prestigious journals and conference proceedings. His research topics of interest are mainly in intelligent management and forecast energy consumption and renewable generation in buildings, building IED communication and integration and ocean wave energy conversion. Ramiro Morais was born in Lisbon, Portugal in 1984. He obtained his Masters Degree In Electrical Engineering and Computers in 2013, in the Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa (FCT/UNL). His thesis is about energy efficiency and automation entitled “Plataforma de previsão do consumo elétrico para edifcios” has contributed to the article “Load forecasting on intelligent buildings based on short-term occupancy monitoring”.
J.A. Oliveira-Lima et al. / Energy and Buildings 116 (2016) 512–521
J.F. Martins (M’96-SM’08) was born in Lisbon, Portugal, in 1967. He received the B.S., M.Sc., and Ph.D. degrees in electrical engineering from the Instituto Superior Técnico, Technical University of Lisbon, Lisbon, in 1990, 1996, and 2003, respectively. Currently, he is an Assistant Professor with the Department of Electrical Engineering, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa (FCT/UNL), Caparica, Portugal. He also works for the Center for Technologies and Systems, UNINOVA. He has published more than 30 scientific articles in prestigious journals and books and more than 100 articles as references for conference proceedings. His research interests are mainly in control of electrical drives and advanced learning control techniques for electromechanical systems, grammatical inference learning algorithms, fault diagnosis and fault tolerant operation, alternative energies and power quality, intelligent and energy efficient buildings, smart grid integration and related engineering education issues. Adrian Florea obtained his MSE (1998) and his Ph.D. in Computer Science from the ‘Politehnica’ University of Bucharest, Romania (2005). At present he is Associate Professor in Computer Science and Engineering at the ‘Lucian Blaga’ University of Sibiu, Romania. Adrian is an active researcher in the fields of High Performance Processor Design and Simulation, Dynamic Branch and Value Prediction. He has published over 6 (didactic and scientific) books and 46 scientific papers about Computer Architecture in some prestigious journals (ISI Web of Science) and international top conferences in Romania, USA, UK, Italy, Germany, China, Slovenia, Korea, Latvia, Spain, India, Poland etc. He received “Tudor Tanasescu” Romanian Academy Award 2005, for the book entitled ‘Microarchitectures simulation and optimization’ (in Romanian) and ‘Ad Augusta Per Angusta’ Award for young researcher, received from ‘Lucian Blaga’ University of Sibiu in June 2007, for special results obtained in scientific research. His Web-page can be found at http://webspace.ulbsibiu.ro/adrian.florea/html/.
521
Celson Lima was born in the Amazon region of Brazil and has over 19 years of experience with European research projects. He received his doctoral degree in 2001 from the Electrical Department of the Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa (FCT/UNL); his master’s degree in Mechanical Engineering in 1994 in a cooperation involving Federal University of Santa Catarina (Brazil) and the Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa (FCT/UNL); he graduated from the Federal University of Santa Catarina with a degree in Computer Science in 1986. Celson worked at Center Scientifique et Technique du Bátiment (CSTB) from April 2001 to December 2007, dealing with the development of software tools and coordination of European Research projects tackling the use of ICT for Building and Construction sector. He was an Assistant Professor in the Electrical Engineering Department from December 2007 to February 2011; during this time Celson was involved in research activities such as the European CoSpaces IP project and the NEMO & CODED Portuguese project. In February 2011, he joined the Federal University of Western Pará, in the heart of the Amazon region in Brasil (his homeland), where he works in the field of Computer Science programming. Celson then headed the Institute of Engineering and Geosciences from June 2012 until November 2013. Currently Celson is a visiting researcher at the MIT IPC, working on a research project involving IPC expertise and the Brazilian industry. Additionally, Celson has also been involved in the organization of international conferences, such as CIB 2010 (W78), CIB 2009 and 2007 (W102), ECPPM 2006, CE 2006, CE 2003, PRO-VE 2006, PRO-VE99. He has published more than 90 articles for journals, conferences, and book chapters.
Contents lists available at ScienceDirect
Energy and Buildings journal homepage: www.elsevier.com/locate/enbuild
Load forecast on intelligent buildings based on temporary occupancy monitoring Jose A. Oliveira-Lima a,∗ , Ramiro Morais a , J.F. Martins a , Adrian Florea b , Celson Lima c a b c
CTS/UNINOVA, Dep. Eng. Electrotécnica, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa (FCT/UNL), Portugal Lucian Blaga University of Sibiu, Sibiu, Romania UFOPA, Instituto de Engenharia e Geociências, Universidade Federal do Oeste do Pará, Santarém, Brazil
a r t i c l e
i n f o
Article history: Received 4 May 2015 Received in revised form 20 January 2016 Accepted 21 January 2016 Available online 22 January 2016 Keywords: Building occupancy Energy consumption forecasting Neural networks Parking lot occupancy Intelligent buildings
a b s t r a c t The modeling of energy consumption in buildings must consider occupancy as a relevant input, since it plays a very important role in the overall building’s energy consumption. Frequently, buildings lack of permanent occupancy monitoring solutions. However, they may include data sources that are correlated with real building occupancy. This study proposes a new methodology for energy consumption modeling, supported by these alternative data sources, such as the number of vehicles in a parking lot. The aim is to mitigate investment in permanent occupancy monitoring solutions. The proposed methodology makes use of short-term real occupancy monitoring for model fitting, to enable the development of occupancy and energy consumption models, based on these alternative data sources. © 2016 Elsevier B.V. All rights reserved.
1. Introduction Buildings are major contributors to world’s energy consumption and CO2 emissions. These infrastructures, which often use more energy than necessary, are still being commissioned every day, and millions of energy inefficient buildings will remain in the upcoming decades. Usually, large buildings are operated by Building Management Systems (BMS), which control several electrical devices spread around the power grid to optimize building energy consumption and user comfort. Often, these BMS are quite inefficient as they only account for energy consumption data or are time-based operated, neglecting several other important factors, such as the number of occupants inside a building (or occupancy profile). The advent of Intelligent Buildings (IB) and the so-called NetZero Energy Buildings (NZEB), with their purpose of producing as much energy as they consume, has set the building energy efficiency goal increasingly higher [1–3]. Intelligent Buildings must be able to learn dynamically, adapting themselves according to several changing factors. Among them we may consider the outside environment, occupancy level and energy consumption, among others [4–6]. The inhabitants’ behavior influences the overall building energy consumption in a high degree and this study relies on the idea that it is possible to predict the electrical load of a building
∗ Corresponding author. E-mail address: [email protected] (J.A. Oliveira-Lima). http://dx.doi.org/10.1016/j.enbuild.2016.01.028 0378-7788/© 2016 Elsevier B.V. All rights reserved.
by using the building occupancy level. The optimization of building energy consumption must, therefore, include occupancy monitoring. Forecast and accurate occupancy profiles are thus required. However, most buildings do not use dedicated systems/devices to monitor occupancy level, due to building age or budget constraints. Nevertheless, in some cases, they may contain other systems that hold data related to the building’s occupancy level. Such systems may provide enough data to estimate approximate occupancy profiles. Some studies estimate building occupancy level by monitoring specific electric loads, such as lighting, Heating, Ventilation and Air Conditioning (HVAC), or other office equipment [7]. Others resort to building IT systems, such as Wi-Fi access points, instant messaging applications, schedules and mobile devices [8]. Since alternative occupancy estimation methods do not have access to real occupancy data, they often share the same problem: lack of accuracy. This paper proposes a new methodology to enable the estimation of occupancy profiles supported by alternative data sources, improving the accuracy and feasibility of both the estimation of building occupancy profile and energy consumption. The proposed methodology combines long-term coverage data sets of alternative occupancy data sources and building energy consumption with temporary real occupancy data acquisition, in order to fit the profiles of the estimated building occupancy level. It combines these short-term coverage and long-term coverage data sets with machine learning algorithms, such as Artificial Neural Networks (ANNs), in order to create both building occupancy and energy consumption profiles. The number of vehicles in a parking lot, during
J.A. Oliveira-Lima et al. / Energy and Buildings 116 (2016) 512–521
a stated period of time, is used as an Alternative Data Profile (ADP), emphasizing its potential in order to be used as a load forecast parameter and as an alternative data source for building occupancy representation. This work makes two major contributions: (a) it allows to accurately model real occupancy by the means of using alternative data sources. Contrary to most studies, this work doesn’t model sensor data that is related with real occupancy, but actually models real building occupancy with alternative data sources; and (b) it uses occupancy estimation (and not real occupancy) to model building energy consumption. In the following sections, the information is organized as follows: in Section 2 we review the Related work in the field of building load and prediction tools applied to estimate it. Section 3 describes the several stages to apply the proposed methodology to improve the estimation of occupancy profiles supported by alternative data sources, whereas Section 4 presents the experimental setup in order to determine the occupancy level of the building and of the parking lot. Laborious simulation results are illustrated in Section 5 to validate the proposed methodology. Finally, Section 6 suggests directions for future work and concludes the paper. 2. Related work 2.1. Building occupancy pattern and forecast For more than 20 years, the research in artificial neural networks has proved their usefulness in pattern recognition and pattern classification (forecast being a classification problem). In this section we review some approaches that are related to our work through the tools, applicability domain, learning algorithm or the methods of collecting the data sets required for prediction. In [9] the authors analyzed neural prediction techniques used in ubiquitous computing application. They predict the next visited room based on the history of rooms, by a certain person who moves within an office building. Occupancy prediction involves home heating control and efficient management of energy. In [10] occupancy sensing and historical occupancy data are used to estimate the probability of future occupancy profile, allowing the home to be heated only when necessary. In [11] the authors implement a multi-modal sensor agent, nonintrusive and low-cost, that combines information such as motion detection, CO2 reading, sound level, ambient light, and door state sensing, in order to estimate accurately the number of occupants in each room using different machine learning techniques (MLP and Support Vector Machines). 2.2. Occupancy determination methods The occupancy profile of a building may be obtained directly or indirectly. Directly, when the entrance and exit events are accounted for, and indirectly, if the number of occupants is obtained or estimated from other factors that may represent this number. Martani determined the occupancy level according to the number of wireless connections for each instant, concluding that it reflects the occupancy profile, leading to an electric load variation of over 60% [12]. Liao and Barooah indicate other indirect ways of determining occupancy profile: video cameras with people counting software, optical tripwires and PIR (pyro electric infrared), motion sensors for particular areas or CO2 , just to name a few [13]. Ekwevugbe used multiple sensors for redundancy in order to obtain more accurate results [14]. This approach emphasizes the effectiveness and benefits of low cost instrumentation to monitor the occupancy
513
level. The inaccuracy of the sensors can lead to residual or negative values of occupancy level the end of each day. As a solution to minimize this factor and eliminate possible future errors, Hutchins has proposed a method to eliminate these propagation errors between days, by resetting the occupancy level to zero at the end of each day, and including a clause according to which the occupancy level cannot be inferior to zero, thus preventing negative, unrealistic values [15]. In [16] a learning algorithm was used to learn the behavior of an occupant in single person office based on motion sensor data. The learned model predicts the presence and absence of the occupant with 80–83% accuracy. The occupancy data has only two states: occupied and non-occupied, and the accuracy is calculated as a Mean Absolute Error (MAE), which, per definition, is always lower than the MSE for the same data analysis. In [17] an auto-regressive hidden Markov model (ARHMM), is investigated to estimate the number of occupants in a research laboratory in a building using a wireless sensor network, and compared with a simple hidden Markov model (HMM). The authors use the MAE to assess the models’ accuracy, reaching values of 25.2–76.2% accuracy for the HMM and 80.1–84% accuracy with the ARHMM. The ARHMM shows better accuracy when the occupancy changes frequently. The stated studies assess the performance of learning algorithms in modeling occupancy using different sensor types. The model accuracy is calculated by comparing real sensor data with simulated model data. However, none of these methods assess model accuracy in terms of real occupancy data and real occupancy estimation. This work makes a contribution concerning this goal, by proposing a methodology to model real occupancy with alternative data sources. It changes the modeling approach, since the accuracy of the model isn’t assessed with alternative data source data, but instead with real occupancy data.
2.3. Main factors affecting building load In order to perform an accurate forecast of a building’s electrical load, it is necessary to know which factors affect its performance. Many studies have been made on that subject [18–20]. Leung states that cooling is responsible for approximately 45% of a building’s electric load [7]. The four main factors that affect the cooling demand of a building are: its own characteristics (location, orientation and type of building); systems designed to provide better conditions to occupants (HVAC systems, lighting); outdoor environmental factors (weather conditions such as temperature, humidity and wind) and occupants’ activities (human behavior). This last factor, human occupancy, is often of random nature and very hard to predict. Windows opening, manual HVAC parameters adjustment or lighting systems are some of the random variables that also aggravate the forecast of the building load [18]. Besides their behavior, the number of occupants at a given moment also influences the building load. Al-Mumin concluded, through a study about building occupancy profile in Kuwait, that the occupants’ lifestyle influences the building annual load [21]. The work hours, implying workers being away from home during the day, leads to different occupancy profiles between residences and workplaces. Therefore, the day type (holiday, weekend, weekday) and the daytime have a huge influence on the building’s load and should be considered as deterministic variables in related studies. The methodology proposed in this work makes a contribution to building energy consumption estimation by enabling to use data sources that may be related with building occupancy, to estimate its impact in overall building energy consumption.
514
J.A. Oliveira-Lima et al. / Energy and Buildings 116 (2016) 512–521
3. Proposed approach This work exploits correlations and ratios between distinct but related time-series data sets, aiming to enable the prediction and estimation of both building occupancy and building energy consumption. The prediction of building energy consumption is achieved by using an ADP that is indirectly related to building energy consumption and, simultaneously, directly related to building occupancy. Also, there should be enough data to model long-term coverage time-series profiles of such ADP. Data monitoring plays an important part in this process, building data sets from the ground up and continuously updating them. Fig. 1. ROP/ADP time-varying ratio.
3.1. Stage 1: Occupancy and energy consumption modeling The first stage of the proposed approach consists in data acquisition at three different levels, namely: (i) real building occupancy is temporarily monitored to support the learning of the Real Occupancy Profile (ROP); (ii) the ADP is permanently monitored to permanently build and update a long-term coverage data set and to support correlational reasoning together with the ROP; and (iii) Building Energy Consumption (BEC) is also permanently monitored to continuously build and update a long-term coverage data set and to support correlational reasoning with the ADP. ROP and ADP are specified as follows:
Fig. 2. Modeling building occupancy using an ADP and a ROP/ADP model.
• ROP(t,D,H,is Holiday) – real building occupancy over time (nonlinear) and represents a positive integer number (of persons) measured inside a building at each hour for each day. • ADP(t,D,H,is Holiday) = number of vehicles over time (nonlinear function the number of cars at each hour for each day measured outside of building, in the parking lot) where: • t = the number of day starting from 1 to 56 (8 weeks meaning of short-term data set) • D = the day of the week (Monday to Sunday) • H = the hour of the day (from 0 to 23) • is Holiday = the binary variable which defines the day as a holiday (Saturday, Sunday or vacations, national or religious free days) or not (Monday to Friday). The proposed strategy relies on three different data sets: a shortterm coverage data set of the real number of building occupants over time; a long-term coverage data set that is directly related to building occupancy profile; and a long-term coverage data set covering the building energy consumption over time.
3.2. Stage 2: Correlating data The second stage consists in generating a nonlinear function for the ROP and ADP data sets and comparing them during the same period of time. This gives the time-varying ratio of ROP/ADP, as shown in Fig. 1. The ratio between the ROP and the ADP should provide the estimation for the nonlinear function of persons per unit of ADP. The result is a short-term coverage data set with a time-varying ratio ROP/ADP, based on real data. This is a short-term coverage profile that relates the building occupancy level with units of ADP, as follows: • ROP/ADP(t,D,H,is Holiday) – persons per car function (nonlinear) obtained by dividing the number of occupants by the number of cars in the parking lot at each hour for each day during the short-term.
Persons per Car ROP ROP(t, D, H, is Holiday) = ADP(t, D, H, is Holiday) ADP(t, D, H, is Holiday)
(1)
In this work, a short-term data set of the real occupancy of the building is obtained by developing and using a temporary occupancy monitoring module. This approach is intended to prove that only a short-term period of real occupancy monitoring is required to develop a model of the building energy consumption, based on a long-term ADP. The ADP was developed based on the occupancy of the parking lot of a university campus, and was used to model the energy consumption of one building that belongs to that campus. 3.3. Stage 3: Profiling and extrapolation The applicability of the ROP/ADP ratio can be extended to bigger data sets by using the insight provided by short-term coverage data analysis. Short-term coverage data sets based on real observations support the development of models that make use of long-term coverage data sets of ADP to improve the forecast ability on longterm coverage building occupation. The third stage consists in using machine learning techniques to enable the learning of ROP/ADP models, thus allowing the building occupancy forecast for longterm coverage data sets of ADP. In this study ANNs are used to generate these models. As shown in Fig. 2, the short-term coverage data set of ROP/ADP is used to train the ANN and the ROP/ADP profile is modeled. Relevant features of ROP/ADP profile are then categorized into mean based profiles (e.g. daily, weekend, holiday profiles) and multiplied by the same profiles of the ADP long-term coverage data set. The number of occupants inside the building for past, present and future ADP data can then be calculated by developing profiles in terms of number of persons per unit of ADP at each hour. The result is a long-term coverage data set of building occupancy level, based on a trained ROP/ADP model:
J.A. Oliveira-Lima et al. / Energy and Buildings 116 (2016) 512–521
515
4. The experimental setup 4.1. Determining the occupancy profile of the building
Fig. 3. Modeling of building electrical load.
• ROP/ADP estimated(t + t,D,H,is Holiday) – the estimative function of the persons per car during the long-term.
The ADP can be the result of a real data set or of a trained model. In case the ADP results from a trained model, the outcome consists of the estimated future building occupancy profile. Being able to predict the future ADP (using ANNs or other learning technique) it is possible to predict the future number of occupants inside the building. An ANN was used in order to develop a ROP/ADP model based on data sets of building and parking occupancy levels. The purpose is to obtain a profile that enables us to forecast the electrical load for other time frames, using the number of vehicles in the parking lot. The ANN was developed using MATLAB’s Neural Network Toolbox due to its ease of use and capability of providing good results. The designed ANN is a Multilayer Perceptron (MLP), a feedforward network having one-way connections from input to output layers that is widely used to solve pattern classification problems. The model to estimate the number of persons per car was obtained by using inputs such as the days of the week, the time of the day and holidays. The proposed approach is intended to be independent of the modeling algorithm. The ANN was used with the purpose of validating the proposed approach.
3.4. Stage 4 – Load forecast According to the premise of this study, the electrical load of a given building depends on the building occupancy profile over time. Thus, electrical load can be correlated with building occupancy level, and reasoning models and tools that use occupancy as input can provide load estimations and previsions. The fourth stage consists in using machine-learning methods to build up models, that learn load profiles based on the building occupancy profile obtained from the combination of both short-term coverage ROP and long-term coverage ADP, as shown in Fig. 3. Long-term coverage data sets of both the real BEC over time and the estimated building occupancy level are required. In this work a MLP ANN was used to model the building energy consumption. The estimation of the building occupancy was used as input, together with time-related variables. This occupancy estimation was obtained by using the parking lot occupancy as ADP. This approach improves the BEC model accuracy by relying on an input (occupancy estimation) that results from a model fitted with real occupancy data, instead of using directly an ADP (parking lot occupancy), with potentially increased error. Again, the proposed approach is independent from the learning algorithm used. The ANN was used for validation purposes only.
This proposed methodology was implemented and tested in the campus of the Electrical Engineering Department of the Faculty of Sciences and Technology, Nova University of Lisbon. The building is located inside the University campus, which holds several car entrances monitored by a security system. The entrance and exit of vehicles is registered and allocated to student/teacher/college/course. This comprehensive data set of parking lot occupancy over time was used as ADP in this study. Also, the building’s real occupancy profile was monitored by a portable module for occupancy monitoring, developed for this study and installed at the building’s entrance, in order to determine the building’s ROP. This module consists of two laser beams Wenta Electronic LM-102-B119 aligned with two light dependent resistors (LDR) whose voltage is measured by an Arduino Duemilanove microcontroller board, with an ATmega328 controller. When one of the beams is interrupted by the presence of a person in front of it, the LDR resistance increases, causing a voltage drop. This voltage drop indicates that a person is entering or exiting the building. To determine the direction of the event, two beams are used. According to which beam is firstly interrupted, the movement will show entry or exit. Both entrance and exit events are recorded by the Arduino and sent to a computer via serial connection. The structure of the information stored consists of: (i) date and time; (ii) the total number of occupants of the building for a given instant; (iii) the total entry and exit events since initialization; and (iv) the number of timeouts since the data was last saved. Each timeout represents an incomplete beam event processing and is triggered whenever one of the beams is interrupted and if the other one is not interrupted during a 4 second period of time. Similarly, to other PIR-based systems, this module isn’t noise free. To improve the system’s accuracy, avoiding negative occupancy values and error propagation, the lowest total number of occupants is limited to zero and reset every day at 5 a.m. Fig. 4 shows the average weekly value for eight weeks of data acquisition. As depicted by this plot, Thursday is the day with the highest occupancy level, while Monday, Tuesday and Friday have similar occupancy levels. Wednesday has around half the occupancy level of other weekdays and the weekends have very little occupancy levels. In the horizontal axis a small vertical line segment denoting each weekday marks noon. 4.2. Parking lot occupancy as ADP This study comprises the evaluation of using the number of vehicles over time as an ADP to the proposed load forecast strategy. Particularly, it uses the parking lot occupancy profile from the University campus as ADP to estimate the number of persons inside a building over time. The parking lot is shared with several buildings that exist in the university campus. Thus, the ratio between the building and the parking lot occupancy levels (ROP/ADP) varies. The ADP data set contains all the entry/exit events recorded for all the campus’ gates during nine months’ time. These events are relative to the students, professors and staff of the Electrical Engineering department. The charts of the same eight weeks of data of ROP and ADP are represented in Fig. 5. The person per car function was obtained by dividing the number of occupants by the number of cars in the park, for each sample. By examining the plot in Fig. 5, it is possible to relate the building occupancy level represented by the top graphic plot, with the parking lot represented in the middle. Although both functions do not overlap, they display similar behavior and slopes in the majority of samples. The reason why they do not overlap is due to a large
516
J.A. Oliveira-Lima et al. / Energy and Buildings 116 (2016) 512–521
Fig. 4. Weekly average building’s occupancy.
Fig. 5. Building occupancy (top) vs. parking lot occupancy (middle) vs. person per car (bottom).
J.A. Oliveira-Lima et al. / Energy and Buildings 116 (2016) 512–521
517
Fig. 6. Electrical load monitoring setup.
number of students from this department having classes in other buildings and, therefore, may be accounted for in the parking lot but not in the building. The number of person per car represented by the bottom graphic plot shows some unrealistic peaks, occurring usually at around 8 a.m. The most evident is in the 6th day of samples with a peak of 64 persons per car. These are caused by the arrival at the car park of the first car of the day, having some people already registered in the building; they arrived by another means of transport or are not associated with the department but still have activity in the building. The correlation between the number of vehicles over time and time-related parameters, such as day of week, hour of day, and type of day was assessed, in order to validate the potential of using a learnt model of parking lot occupancy as ADP to support forecasts of future building occupancy level. A nine months’ data set of the number of vehicles over time in the parking lot was used
Fig. 7. ANN1 schematic used to develop a general profile of person per car.
and the Pearson product-moment correlation coefficient was calculated in 0.842, which reveals that there is a considerable correlation between the number of vehicles and the above-mentioned timerelated parameters.
Fig. 8. Actual values of person per car (top) vs. forecast results (bottom).
518
J.A. Oliveira-Lima et al. / Energy and Buildings 116 (2016) 512–521
Fig. 9. Recorded occupancy (top) vs. forecasted occupancy with 6 weeks data (bottom).
4.3. Electrical load monitoring The HVAC binary variable and the historical electrical load were obtained by installing an electrical load monitoring system in the Low Voltage Distribution Board (LVDB) of the building, as shown in Fig. 6. This monitoring system was conceived by installing two Algodue UPT210 multi-function energy meters, one for the general circuit and the other for the HVAC circuit. The acquired data was sent to a local computer running a JAVA application developed for this purpose. 5. Results 5.1. Building occupancy patterns using parking lot data An ANN1 was used in order to develop a ROP/ADP model based on the eight-week data sets of building and parking occupancy levels. The ANN1 was designed with 4 inputs, 10 neurons in hidden layer and 1 linear neuron in the output layer. The activation function is sigmoidal. The training process is conducted with the well-known Levenberg–Marquardt backpropagation algorithm, used for fast-supervised learning in feedforward networks using the Mean Squared Error (MSE) and gradient descent method. The inputs for ANN1 are days of the week, the time of the day, the binary variable which defines the day as a holiday or not, and the person per car obtained previously by dividing the occupancy level
Table 1 Average MSE and correlations of the 5 simulations with 8 weeks data. Training
Validation
Testing
MSE
R
MSE
R
MSE
R
8.91E−02
7.87E−01
9.00E−02
7.83E−01
9.21E−02
7.81E−01
of the building by the parking lot occupancy profile. The output is the forecast profile of person per car for the evaluated period. The training process used 70% of samples for training, 15% for validation and also 15% for testing because it seems to provide the smaller mean square error performance. Each simulation was run five times and the results were averaged. The layout of the developed ANN is represented in Fig. 7. The top curve of Fig. 8 represents the actual values obtained by dividing the occupancy level by the number of cars, and the bottom curve represents the forecasts, which are the ANN outputs. The performance of the ANN is represented in Table 1. To evaluate the model, the MSE and correlation (R) were computed with Matlab. MSE expresses the difference between the real output values and those generated by the ANN. The correlation coefficient is calculated by Pearson’s correlation coefficient, measuring the correlation between the real output values and the ones generated by the ANN. Both obtained MSE and correlation values show that the learnt model is reasonably accurate.
J.A. Oliveira-Lima et al. / Energy and Buildings 116 (2016) 512–521
519
Table 2 Average MSE and correlations of the 5 simulations with 6 weeks data. Training
Validation
Testing
MSE
R
MSE
R
MSE
R
5.82E−02
8.53E−01
6.06E−02
8.49E−01
6.08E−02
8.46E−01
Fig. 10. Overall schematic for the ANNs used with its inputs/outputs.
These results are related to the data acquired during the evaluation period. In order to extend the forecast of the building occupancy level to nine months’ time, the learnt values were split into a week-based profile. In order to do that, the forecasted values were processed to calculate the hourly average person per car for each day of the week. This average was then multiplied by the nine months’ data of the parking lot occupancy profile, resulting in the forecast of the building occupancy level for nine months’ time. In order to demonstrate the validity of this forecast, another ANN was developed, with similar characteristics to the previous one, with only six weeks of data. This way, two weeks of actual data can be used to validate the model. The obtained results are represented in Fig. 9. The performance of this ANN is represented in Table 2. Both the MSE and the correlation values show improved results. We can see that the values of R are of above 0.781.
5.2. Building load pattern In the previous subsection, the building occupancy forecast was obtained for nine months’ time. In this subsection another ANN was developed to forecast the building’s electric load for the same period of time. The overall ANN schematic diagram is represented in Fig. 10. The left part of the schematic diagram corresponds to
the one used in the previous section to forecast the person per car during nine months’ time. In order to model the occupancy-based electric load of the building, an ANN2 was developed with the following inputs: day of the week, time of day, binary variable describing the day as a holiday, the HVAC binary variable indicating its operation and the historical electrical load (represented as real electric load). The output was the electrical load forecast for nine months’ time. The samples were obtained between the beginning of October and the end of June. From December 3rd to June 4th the HVAC system has never been activated, and during the remaining period it operated at random cycles of 30–48 min, followed by idle states of approximately one hour. Therefore, it was concluded that its activation did not depend on the building occupancy level or temperature. Five simulations were run with all the inputs from Fig. 10. The results were averaged and this average represents the building’s electric load forecast for nine months’ time. The results are presented in Fig. 11. The electric load was normalized between 1 and 0, 1 represents the maximum and 0 the minimum. The forecasted load (top plot) and the actual load (bottom plot) are almost coincident, near to zero error (5,43E−8), showing a very accurate prediction. Samples from 1150 to 4515 correspond to the period of time when the HVAC system was turned off. In order to demonstrate the accuracy of the forecasts, the MSE and
Fig. 11. Forecasted load with HVAC data (top) vs. actual load (bottom).
520
J.A. Oliveira-Lima et al. / Energy and Buildings 116 (2016) 512–521
Table 3 Average mse and correlat ions with 9 Months data. Training
Validation
Testing
MSE
R
MSE
R
MSE
R
4.65E−03
8.43E−01
5.70E−03
8.13E−01
5.74E−03
8.19E−01
correlation averages of the simulation with HVAC data are represented in Table 3. 6. Conclusion This study presents a new methodology to forecast long-term coverage building electrical loads using temporary building occupancy monitoring. This methodology focuses on learning models that make use of long-term coverage occupancy ADPs and electrical load data sets, together with short-term coverage ROPs. ADPs are supported by data sources that are highly correlated with building occupancy level. For this research a nine months’ data set of the number of vehicles over time in a shared parking lot was used as ADP. A ROP was obtained by determining the occupancy profile of the building for a short period of time through the use of a temporary device installed at its entrance. The number of vehicles in the parking lot during a stated period of time shows a very high correlation with the same time-related parameters that were used to model the ROP. An ANN model was developed and made to generate the ROP/ADP profile. This model proved to be accurate due to its high correlation and very low MSE, and so was used to represent, in an indirect manner, the occupancy level of the building. Comparing the model accuracy results with the results from other occupancy modeling studies that use alternative data sources [16] [17], we can conclude that the proposed approach is able to model building occupancy with very high accuracy. Studies [16] and [17] assess accuracy using MAE. The best accuracy results obtained with these approaches are of 0.84. In this work, although using MSE, which per definition is always higher than the MAE for the same data analysis, we were able to achieve MSE of 9.21E−02 for 8 weeks of real occupancy data. A second ANN was developed to model the building electrical load. This model is supported by the ROP/ADP model and by longterm coverage data sets of ADP and building electrical load during a period of time. This model proved to be reasonably accurate, with high average correlation and low average MSE, which indicates that the method studied here is valid and can produce accurate and reliable results. From these results, it is concluded that the proposed methodology is able to model occupancy using alternative data sources, and that the building’s parking lot occupancy level can be used as a parameter for electric load forecast regarding a building, as the electric load profile is identical to the actual one, the forecast error obtained is very low and the correlation very high. As a future work, this study will be conducted in other institutions with different ADPs and compared to this particular case. Also, applying a similar methodology on other energy efficient applications, such as the estimation of the energy consumption of electrical circuits from Intelligent Buildings is another suggestion for a future study. References [1] I. Sartori, A. Napolitano, K. Voss, Net zero energy buildings: a consistent definition framework, Energy Build. 48 (2012) 220–232. [2] International Energy Agency (IEA), Energy Technology Perspectives 2010: Scenarios and Strategies to 2050, International Energy Agency Publications, 2010. [3] M. Sechilariu, B. Wang, F. Locment, Building-integrated microgrid: advanced local energy management for forthcoming smart power grid communication, Energy Build. 59 (2013) 236–243.
[4] Z. Zhao, W.C. Lee, Y. Shin, K.-B. Song, An optimal power scheduling method for demand response in home energy management system, IEEE Trans. Smart Grid 4 (Sep (3)) (2013) 1391–1400. [5] D. He, W. Lin, N. Liu, R.G. Harley, T.G. Habetler, Incorporating non-intrusive load monitoring into building level demand response, IEEE Trans. Smart Grid 4 (Dec (4)) (2013) 1870–1877. [6] J.H. Yoon, R. Baldick, A. Novoselac, Dynamic demand response controller based on real-time retail price for residential buildings, IEEE Trans. Smart Grid 5 (Jan (1)) (2014) 121–129. [7] M. Leung, N.C. Tse, L. Lai, T. Chow, The use of occupancy space electrical power demand in building cooling load prediction, Energy Build. 55 (2012) 151–163. [8] S.K. Ghai, L.V. Thanayankizil, D.P. Seetharam, D. Chakraborty, Occupancy detection in commercial buildings using opportunistic context sources, in: IEEE International conference on pervasive computing and communications workshops. IEEE, Mar 2012, 2012, pp. 463–466. [9] L. Vintan, A. Gellert, J. Petzold, T. Ungerer, Person movement prediction using neural networks, in: First workshop on modeling and retrieval of context, 2004. [10] J. Scott, A. Bernheim Brush, J. Krumm, B. Meyers, M. Hazas, S. Hodges, N. Villar, PreHeat: controlling home heating using occupancy prediction, ser, UbiComp’11, ACM, 2011, pp. 281–290. [11] S. Mamidi, Y.-H. Chang, R. Maheswaran, Improving building energy efficiency with a network of sensing, learning and prediction agents, in: Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems vol. 1, ser. AAMAS, International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 2012, pp. 45–52 [Online] Available: http://dl.acm.org/citation.cfm?id=2343576.2343582. [12] C. Martani, D. Lee, P. Robinson, R. Britter, C. Ratti, Enernet: studying the dynamic relationship between building occupancy and energy consumption, Energy Build. 47 (2012) 584–591. [13] C. Liao, P. Barooah, An integrated approach to occupancy modeling and estimation in commercial buildings, 2010, pp. 3130–3135. [14] T. Ekwevugbe, N. Brown, D. Fan, A design model for building occupancy detection using sensor fusion, in: [6th] IEEE International conference on digital ecosystems and technologies (DEST). IEEE, Jun 2012, 2012, pp. 1–6. [15] J. Hutchins, A. Ihler, P. Smyth, Modeling count data from multiple sensors: a building occupancy model, in: 2nd IEEE International workshop on computational advances in multi-sensor adaptive processing. IEEE, Dec 2007, 2007, pp. 241–244. [16] A. Agnetis, G. de Pascale, P. Detti, A. Vicino, Load scheduling for household energy consumption optimization, IEEE Trans. Smart Grid 4 (Dec (4)) (2013) 2364–2373. [17] T. Yu, Modeling occupancy behavior for energy efficiency and occupants comfort management in intelligent buildings, in: 2010 Ninth International conference on machine learning and applications, 2010, pp. 726–731. [18] B. Ai, Z. Fan, R.X. Gao, Occupancy estimation for smart buildings by an auto-regressive hidden Markov model, in: 2014 American Control Conference, 2014, pp. 2234–2239. [19] A. Badri, Z. Ameli, A. Birjandi, Application of artificial neural networks and fuzzy logic methods for short term load forecasting, Energy Proc. 14 (2012) 1883–1888. [20] A. Marvuglia, A. Messineo, Using recurrent artificial neural networks to forecast household electricity consumption, Energy Proc. 14 (2012) 45–55. [21] A. Al-Mumin, O. Khattab, G. Sridhar, Occupants’ behavior and activity patterns influencing the energy consumption in the Kuwaiti residences, Energy Build. 35 (Jul (6)) (2003) 549–559. Jose A. Oliveira-Lima was born in Lisbon, Portugal, in 1984. He obtained his B.S. and M.Sc. degrees in Electrical Engineering and Computing from the Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa (FCT/UNL), Lisbon, in 2006 and 2010, respectively, and he is concluding his PhD in the same faculty. He is also a researcher in the Center for Technologies and Systems, UNINOVA and was involved in research activities such as the NEMO & CODED and PRODUTECH PSI projects. He has published more than 15 scientific articles in peer-reviewed and prestigious journals and conference proceedings. His research topics of interest are mainly in intelligent management and forecast energy consumption and renewable generation in buildings, building IED communication and integration and ocean wave energy conversion. Ramiro Morais was born in Lisbon, Portugal in 1984. He obtained his Masters Degree In Electrical Engineering and Computers in 2013, in the Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa (FCT/UNL). His thesis is about energy efficiency and automation entitled “Plataforma de previsão do consumo elétrico para edifcios” has contributed to the article “Load forecasting on intelligent buildings based on short-term occupancy monitoring”.
J.A. Oliveira-Lima et al. / Energy and Buildings 116 (2016) 512–521
J.F. Martins (M’96-SM’08) was born in Lisbon, Portugal, in 1967. He received the B.S., M.Sc., and Ph.D. degrees in electrical engineering from the Instituto Superior Técnico, Technical University of Lisbon, Lisbon, in 1990, 1996, and 2003, respectively. Currently, he is an Assistant Professor with the Department of Electrical Engineering, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa (FCT/UNL), Caparica, Portugal. He also works for the Center for Technologies and Systems, UNINOVA. He has published more than 30 scientific articles in prestigious journals and books and more than 100 articles as references for conference proceedings. His research interests are mainly in control of electrical drives and advanced learning control techniques for electromechanical systems, grammatical inference learning algorithms, fault diagnosis and fault tolerant operation, alternative energies and power quality, intelligent and energy efficient buildings, smart grid integration and related engineering education issues. Adrian Florea obtained his MSE (1998) and his Ph.D. in Computer Science from the ‘Politehnica’ University of Bucharest, Romania (2005). At present he is Associate Professor in Computer Science and Engineering at the ‘Lucian Blaga’ University of Sibiu, Romania. Adrian is an active researcher in the fields of High Performance Processor Design and Simulation, Dynamic Branch and Value Prediction. He has published over 6 (didactic and scientific) books and 46 scientific papers about Computer Architecture in some prestigious journals (ISI Web of Science) and international top conferences in Romania, USA, UK, Italy, Germany, China, Slovenia, Korea, Latvia, Spain, India, Poland etc. He received “Tudor Tanasescu” Romanian Academy Award 2005, for the book entitled ‘Microarchitectures simulation and optimization’ (in Romanian) and ‘Ad Augusta Per Angusta’ Award for young researcher, received from ‘Lucian Blaga’ University of Sibiu in June 2007, for special results obtained in scientific research. His Web-page can be found at http://webspace.ulbsibiu.ro/adrian.florea/html/.
521
Celson Lima was born in the Amazon region of Brazil and has over 19 years of experience with European research projects. He received his doctoral degree in 2001 from the Electrical Department of the Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa (FCT/UNL); his master’s degree in Mechanical Engineering in 1994 in a cooperation involving Federal University of Santa Catarina (Brazil) and the Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa (FCT/UNL); he graduated from the Federal University of Santa Catarina with a degree in Computer Science in 1986. Celson worked at Center Scientifique et Technique du Bátiment (CSTB) from April 2001 to December 2007, dealing with the development of software tools and coordination of European Research projects tackling the use of ICT for Building and Construction sector. He was an Assistant Professor in the Electrical Engineering Department from December 2007 to February 2011; during this time Celson was involved in research activities such as the European CoSpaces IP project and the NEMO & CODED Portuguese project. In February 2011, he joined the Federal University of Western Pará, in the heart of the Amazon region in Brasil (his homeland), where he works in the field of Computer Science programming. Celson then headed the Institute of Engineering and Geosciences from June 2012 until November 2013. Currently Celson is a visiting researcher at the MIT IPC, working on a research project involving IPC expertise and the Brazilian industry. Additionally, Celson has also been involved in the organization of international conferences, such as CIB 2010 (W78), CIB 2009 and 2007 (W102), ECPPM 2006, CE 2006, CE 2003, PRO-VE 2006, PRO-VE99. He has published more than 90 articles for journals, conferences, and book chapters.