Energy saving estimation for plug and lighting load using occupancy analysis

Renewable Energy 143 (2019) 1143e1161

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Renewable Energy journal homepage: www.elsevier.com/locate/renene

Energy saving estimation for plug and lighting load using occupancy analysis Prashant Anand a, *, David Cheong a, Chandra Sekhar a, Mattheos Santamouris b, a, Sekhar Kondepudi a a b

Department of Building, School of Design and Environment, National University of Singapore, Singapore 117566, Singapore Faculty of Built Environment, University of New South Wales, Sydney, NSW 2052, Australia

a r t i c l e i n f o

a b s t r a c t

Article history: Received 31 January 2019 Received in revised form 27 April 2019 Accepted 22 May 2019 Available online 23 May 2019

The gap between the actual and intended energy use for a building is often attributed to stochastic behaviour of occupants. This study systematically investigates the relationship of occupancy with plug and lighting loads energy consumption for several spaces of an institutional building floor. A new parameter ‘Energy-use per person (K)’ is introduced to explain the stochastic relationship between Energy and Occupancy. A model for K is developed as a function of occupancy using ‘Multiple non-linear regression (MNLR)’ and ‘Deep neural network (DNN)’ based algorithms. DNN algorithm shows a better prediction of K with less Mean absolute percentage error (MAPE) of 9.67% and 2.37% compared to 10.34% and 3.15% of MNLR for plug and lighting loads respectively. The model developed is used to estimate possible energy savings during occupied hours with a rule-based energy-use behaviour. Possible plug load energy savings are 8.9%, 3.1% and 1.3% for the classroom, open office, and computer room respectively. Similarly, possible lighting load energy savings are 65.1%, 43.6% and 38.4% for the classroom, open office, and computer room respectively. The study outcome, a robust and iterative ‘K model’ development process can be used as a support tool in decision making for facility management. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Occupancy Deep neural network Energy-use per person Energy saving Plug load Lighting load etc

1. Introduction The International Energy Agency (IEA) has highlighted six factors that influence the building energy efficiency: climate, building envelope, Mechanical and Electrical (M&E) Systems, indoor design criteria, operation and maintenance, and occupant behaviour [1]. Several studies have been conducted in past to investigate the influence of each factor on building energy use [2e17]. Studies have found that the relationship between weather variables and electric power demand exists even at microgrid scale [2e4]. Another study found that the energy efficiency of M&E systems mainly depends on the configuration and operational scenario [5,6]. Similarly, a few studies suggest that the thermal comfort and energy efficiency of a space depends on its use type, indoor design and building envelope configuration [7e9]. It has also been reported that the maintenance and restoration of the building energy systems can significantly

* Corresponding author. Department of Building, School of Design and Environment, National University of Singapore, 4 Architecture drive, 117566, Singapore. E-mail addresses: [email protected], [email protected] (P. Anand). https://doi.org/10.1016/j.renene.2019.05.089 0960-1481/© 2019 Elsevier Ltd. All rights reserved.

improve its efficiency [10,11]. Several studies have been conducted in the past to forecast energy use based on these factors [12,13]. Apart from the aforementioned studies, occupant behaviour has been identified as a key factor affecting energy use inside a building [6,11,14e18]. In a typical building, there are primarily three components that collectively consume 85% of total energy: Air Conditioning and Mechanical Ventilation (ACMV) system, lighting load, and equipment (Plug Loads) [19]. The energy use of these components also varies based on the demand and response of energy markets [20,21]. On an average, plug and lighting loads collectively consume 12e50% of building energy which further increases at a rate of 0.8% per year [22]. Inefficient operation of lighting and plug loads can lead to high wastage of energy during unoccupied hours. A knowledge of interaction between occupants and building systems, i.e. monitoring of plug loads and associated occupant behaviour interventions would lead to better calculation of energy savings [23]. Therefore, occupant information is one of the weakest links in the process of building energy prediction and optimization. Furthermore, a study has found that 26e65% of the energy

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Nomenclature K KPL KLL MNLR DNN ANN EUC P1 P2 P3 P4 P5 P6 P7 MAPE ACMV

Energy use per person Energy use per person plug load Energy use per person lighting load Multiple non-linear regression Deep neural network Artificial Neural Network Energy use cluster Occupancy EUC Space type Class/No Class status Time type Semester status Day type Mean absolute percentage error Air Conditioning and Mechanical Ventilation

in spaces studied (Fig. 1). The plug, lighting and ACMV load data are collected using distribution box level energy meters and Air Handling Unit (AHU) level air flow meters. Unlike many previous studies which discussed occupants and associated energy consumption within a specific space type (e.g., Office workplace) or specific behaviour type (opening and closing of blinds), this study is focussed on occupants and associated energy consumption variations for different spaces [32e36]. An entire floor of an institutional building is selected for this study which includes different types of spaces such as Class Rooms, Computer rooms, Single Office and Open office spaces. These space types constitute all the main spaces in this institutional building. These spaces combined account for 100% of energy use in this building. Analysis of this study has been performed with the objective of identifying possible energy savings during unoccupied and occupied hours. A new parameter ‘K’ is proposed in this study to simplify the complex association between energy and occupancy as in Eq. (1).

Energy use ¼ consumption occurred during non-operational hours of building, as opposed to the work hours of 07:30 a.m.e04:30 p.m. [24]. These non-operational hours energy consumption could be a significant source of energy wastage. A recent study achieved 20.3% of energy saving by implementing an occupant centric demand-driven control as compared to conventional baseline control [25]. It has been found that the energy saving potential in an individual office is inversely correlated to its occupancy count [25]. Some of the previous experimental and simulation studies found that energy saving of up to 45% could be achieved by using intelligent occupancy sensors [26e29]. In a study of the institutional building using wi-fi counted occupancy, 63e69% variation in total electricity consumption is found over a period of time due to variation in occupant density [29]. The correlation between the occupant density and energy consumption is found to be between 70 and 90% of an office building [16]. Institutional buildings present high variation with respect to occupancy schedules and energy management [30]. Another study of a multipurpose academic building has proposed an occupancy-based energy consumption model to help in the optimization of the building operation schedules along with automatic systems which could achieve a significant energy saving [31]. These studies suggest a strong association between energy consumption and occupancy and therefore it can be concluded that occupancy could be an important parameter in building energy consumption prediction. Energy consumption prediction is essential for facility managers to develop an energy-efficient operational strategy for various building spaces. In view of this, the study presented in this paper focuses on the interaction of occupants with building systems, i.e. usage of plug and lighting loads which collectively consume 43% of total energy

Fig. 1. Energy distribution of floor studied.

X

Occupancy  K

(1)

2. Methodology A four-step method is employed to investigate the impact of occupant behaviour on energy use during occupied and unoccupied hours (Fig. 2). In the first step, time-series data of occupancy (occupant count) and energy consumption (kWh) is collected from various spaces of an institutional building floor. In the second step, the collected data is analysed to investigate the energy saving possibilities during unoccupied hours using a simple energy flow analysis. In the third step, the relation between energy use and occupancy is analysed using K as a function of occupant behaviour along with other parameters such as space type, time, day etc. The model of K is developed using MNLR and DNN algorithms along with k-fold validation. Finally, in the fourth step, K is used to identify the possible energy savings during occupied hours with rule-based energy use behaviour. 2.1. Data collection In this study, space wise data is collected from an entire floor of a 41-year-old institutional building. The building under study is an institutional building located in the tropical climate of Singapore. The floor studied mainly consists of five space types: a) Open offices, b) Closed offices, c) Computer rooms, d) Classrooms and e) Corridors. Fig. 3 is a detailed layout of the floor studied. Maximum design occupancy for each classroom is 30, computer room is 25 and open office is 31 respectively. Time-series data with 5-min interval over a total period of 90 days, spread across two successive semesters (30 days of active teaching period, followed by 30 days during semester break and then 30 days again with active teaching), has been analysed. The data has been collected for all 24 h of the day and the normal building operational hours are between 9:00 a.m. and 10:00 p.m. Table 1 shows the details of various type of plug and lighting loads for the spaces studied. 2.1.1. Occupancy detection Occupancy data is collected using a camera-based technology. This technology counts the occupants by sensing the shape and motion of occupants. Camera-based occupancy counting is a wellestablished method with high accuracy [37,38]. Cameras are installed in each space at such directional angle that they can detect and lock on every occupant and count the number of occupants

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Fig. 2. Research factors and research steps.

Fig. 3. Layout drawing with sensors location.

every 5-min. In this study, a manual counting (ground truth) is also done to validate the camera-based technology and the accuracy of camera-based counting is always found to be greater than 95%. As seen in Fig. 4, this technology is able to detect stationary as well as moving occupants with high accuracy. Location of each sensor is shown in the layout drawing in Fig. 3.

function, is to identify a best-fit trend line that shows highest correlation (R2) between the targeted output (K) and input (Occupancy). This trend line is obtained using a power function model as shown in Eq. (2) where “a” and “a1” are real constant numbers and P1 is occupancy.

2.1.2. Energy disintegration Energy data is collected from distribution box level energy submeters (as in Fig. 5). For each space, two submeters are installed, one for plug load and another for lighting load, with a total 12 submeters which measure the disintegrated lighting and plug energy loads in kWh for each space every 5-min.

K ¼ a$P1a1

(2)

In the next step, other parameters that may influence K, are then investigated as a coefficient of occupancy, to construct an enhanced mathematical model as shown in Eq. (3) for better prediction accuracy, where ‘n’ is the number of parameters.

2.2. Prediction model for K 2.2.1. MNLR model A multiple nonlinear regression model (MNLR) is developed by constructing a best-fit mathematical function which represents the targeted (output) data points as a function of input parameters (variables). The first step for constructing the mathematical

K ¼ a$P1a1 $P2a2 $P3a3 $P4a4 $P5a5 …………::Pnan

(3)

The constants (a1 to an ) are estimated using Least Square Method which estimates the parameters by minimising the squared error (as shown in Eq. (4)) between the predicted and actual outputs.

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Table 1 Plug and lighting load type for spaces studied. Zone type

Classroom

Computer room

Open office

Closed office

Corridor

Plug load type

Lighting load type

Type

quantity

Load

Type

Load

Projector Desktop computer Additional power socket IoT device Projector Desktop Computer/laptops Additional power socket IoT device Desktop computer Office Printer Personal fan Additional socket IoT device Desktop computer Personal Printer Personal fan IoT device Power socket IoT device

3 3 12 3 2 46

13 W/sqm of floor area (approx.)

Fluorescent tube

22 W/sqm of floor area (approx.)

Fluorescent tube

46 2 35 1 16 35 3 10 10 10 10 6 1

Fluorescent tube and table lamp

Fluorescent tube and table lamp

Fluorescent tube

function stated by model function of Eq. (3); Pi and ai are the predictors for observation and regression coefficients. 2.2.2. Deep neural network (DNN) A deep neural network is constructed using five layers of fullyconnected Feed Forward type Artificial Neural Network (ANN) (See Fig. 6a). It consists of one input layer, one autoencoder layer, two hidden layers and one output layer. The logic behind adding an autoencoder is to reduce the noise of input data and to further refine the weights of each node through a nonlinear Principle Component Analysis (PCA) [39]. An auto-encoder is a combination of two layers, namely, an encoder and a decoder (See Fig. 6b). Where, the encoder compresses the input into lower dimension (in form of code) and then the decoder reconstructs the input using code with reduced outliers. The overall process of ANN model development can be broadly split into two subprocesses: a) Training process and b) Prediction process. Fig. 4. Detection of occupancy. n X

½yi  f ðPi ; ai Þ2

(4)

i¼1

here, yi are the actual responses; f ðPi ; ai Þ indicates the nonlinear

2.2.2.1. Training process. The purpose of training a deep neural network is to obtain the weights correlated to each node. The training process starts by randomly initializing the weights for each node and then using a forward pass to calculate the output of each node (See Fig. 6c). Every node takes the sum of weights (w) from their preceding nodes and passes it through a non-linear activation

Fig. 5. Energy meters at floor level distribution box.

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(b)

Eencoder ¼

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1 X1 ðPo  Pr Þ2 þ ðl:Uw Þ þ ðb:Us Þ N 2

(7)

here, E is total error, N is number of samples, Po is measured or desired output, Pr is predicted output, l is weight decay coefficient, Uw is regularization term, b is penalty coefficient of sparsity regularization and Us is sparsity regularization term.

Uw ¼

nl 1 X Sl SX 2 l þ1 1 X wlj;i 2 j i

(8)

l

(a)

Us ¼

S2 X j¼1

S2  X  r KL r k Pbj ¼ rlog rb j¼1

j

!

1r þ ð1  rÞlog 1  rb

! (9)

j

here nl is number of layers, Sl is number of units of layer l, W l is weight of the hidden unit at layer l , and KL is Kullback-Leibler divergence function which measures the similarity between distribution of parameters, while r and b r represent the desired and actual average activation values of a hidden unit respectively. 2.2.2.2. Prediction process. The prediction process is just a forwardpass process with a trained weight associated to each node. In this process, the output of one node becomes the input for nodes in the next layer. The signal flows from left to right, and final output is calculated by performing this procedure for all the nodes.

(c)

2.3. Validation of the model developed Fig. 6. Architecture of DNN.

function (Sigmoid in this study) from left to right. Thus, the weights are outputs of nodes, which are later converted into inputs of nodes in subsequent layers. Final output is the value of the last node. Weighted sum (Z) for each node from the ‘n’ number of nodes is calculated using Eq. (5). It is to be noted that ‘P’ is the predictor for the observation ‘x’.

Z ¼ f ðPi :wÞ ¼ f

n X

! xi wi

(5)

i¼1

Final output is obtained by passing the layer by layer weighted sum from left to right. Further, it is compared with the actual targets of training data to quantify the error (difference between actual and obtained) by means of a loss function. The loss function for training the overall network is based on the Mean square error (MSE) and a regularization term (Uw ) as described in Eqs. (6) and (8). However, the loss function of an auto-encoder is based on the Mean square error of sparsity (MSEsparse ) which has an additional regularization term (Us ). The loss function of the MSEsparse can be presented as Eqs. (7) and (9). Once the error between actual and predicted values is obtained through loss function, a backward pass is performed from right to left to distribute the error among nodes using ‘gradient descent’ backpropagation. By doing this iteratively, a revised weight for each node is obtained with refined MSE and (MSEsparse ). These refined and revised weights are the final node weights used for prediction.

E ¼

1 X1 ðPo  Pr Þ2 þ ðl:Uw Þ N 2

(6)

The entire dataset is divided into two groups: a) Training and testing dataset; and b) Validation dataset. Training and testing dataset for computer room and classroom is the data obtained from Computer room 1 and Classroom 1, whereas validation dataset is the data obtained from Computer room 2 and Classroom 2. However, in the case of open office, 70% of entire data is used for training and testing process and the remaining 30% is used for validation. 2.3.1. k -fold validation as a part of model development process A k-fold validation method is adopted in training and testing process of K prediction model development to enhance the robustness of the model by preventing overfitting. k (equal to 5) is the number of subsets of roughly equal sizes, which are randomly chosen from the entire dataset of training and testing groups. In this method, one subset is used to test the model that is trained using remaining subsets. The accuracy of prediction is estimated through MAPE. 3. Results with discussion 3.1. Energy use flow A disintegrated floor level plug and lighting loads related electrical energy use to the resolution of space type is presented in Fig. 7. Out of total energy use, major share (79%) is lighting load and only 21% is plug load. Out of this 79% lighting load, maximum energy consumption (41.1%) is from lighting load in corridors (common area). This indicates that in spaces such as class rooms, computer rooms and offices of an institutional building, most of the electrical energy is used for providing illumination. Out of 21% plug load, most of the energy is used in open offices (5.8%), computer rooms (5.6%) and closed offices (4.9%). Although computer rooms have more electronic appliances (computers) than open offices, plug load of computer rooms is marginally lower than office space

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unoccupied hours is for the classrooms. Most of the lighting load wastage for classrooms might be from the instances when the student/last person leaving the room after night class forgets to switch off lightings and leaving them ON for the entire night. Largely, the night-time lighting load can be considered as a significant energy wastage which could be saved. However, common spaces like corridors need a certain minimum lighting to be ON even at night because of safety concerns. In such cases, control of lightings through motion detectors could be a potential energy efficient solution. Similarly, unoccupied hour Plug load ranges from 2% for open offices to 12% for computer rooms (Fig. 9). The plug load energy use in unoccupied hours for open office could be considered as a wastage, but the same cannot be said for computer room as most of the times computers are intentionally left in switch-on mode to finish some ongoing computational work. 3.3. Occupancy vs energy Fig. 7. Energy use flow from floor to different spaces.

because the total operational time of computer rooms is very low compared to office spaces. 25. Overall, it has also been noticed that the lighting load is significantly higher than the plug loads. However, for the closed office, the plug load was observed to be higher than lighting load. The aforesaid discrepancy in plug vs lighting loads distribution is attributed to the fact that these spaces have single occupancy and it is common practice to turn off the room lights when not in use. However, the other spaces are either teaching/learning spaces or common offices where there is no individual level control, it is common for these spaces to be illuminated even during unoccupied hours. This distribution of plug and lighting loads among different zones could be considered as an annual distribution, as it has been developed using 90 days of data, with a similar proportion to the semester period (60) and semester-break (30) days. Therefore, based on this division of plug and lighting load among different zones, the annual distribution of plug and lighting load is evaluated to give an overview of data used in the study (see Table 2). Approximately, a total of 259,741.50 kWh of energy is consumed annually by the electrical systems for lighting and plug loads, out of which 54,545.71 kWh is consumed by plug loads and 205,195.78 kWh is consumed by lighting loads. 3.2. Energy use distribution among occupied and unoccupied hours Plug and lighting loads are divided further in occupied and unoccupied hours for different spaces (Fig. 8). Unoccupied hour lighting load ranges from 3% for closed offices to 59% for corridor. In this study, the time-period,23:00 h to 8:00 h, is considered as unoccupied hours, which is based on the typical operational hours of the spaces studied. The second highest lighting load (10%) during

To identify the energy wastage during occupied hours, spaces (classroom, computer room and open office) are investigated further by analysing the association between energy and occupancy. Firstly, the time-series data of energy and occupancy is normalised in the scale of 0e1 using min-max normalization technique (Eq. (10)).

Normalized x ¼

x  minðxÞ maxðxÞ  minðxÞ

(10)

The association between normalised occupancy and normalised energy use is primarily investigated through co-efficient of determination (R2). Overall, the R2 between lighting/plug load energy and occupancy is found very low (0e0.28) for different spaces. Since modelling the existing behaviour of occupant is one of the primary purposes of this study, the outlier points are not removed to improve R2 as these points could represent an energy wastage scenario. So conclusively, this overall low R2 indicates a stochastic relationship between energy use and occupancy, which is difficult to express in the form of a simple (linear) mathematical relationship [33,40]. 3.4. Importance of a new parameter ‘K’ To simplify the relationship between occupancy and energy use, a new parameter named as Energy Use Per Person (K), is introduced in this study. The timeseries data of K is generated by dividing normalised energy use by normalised occupancy for plug and lighting load for each space type and It is found that K has distinct relationship with occupancy. Maximum correlation between Kplug and occupancy is 0.63 for computer room, followed by a correlation of 0.58 for open office and 0.26 for classroom. Similarly, maximum correlation between occupancy and K-lighting is 0.87 for computer room, followed by a correlation of 0.66 for classroom and

Table 2 Annual plug and lighting energy consumption by different zones. Zone type

Total floor area (m2 ) A

B

C

A$(B þ C)

Classroom Computer room Open office Closed office Corridor Total

250 160 200 140 256

34.28 90.90 75.32 90.90 14.20

123.63 165.58 145.55 85.34 417.00

39480.71 41039.16 44156.06 24675.44 110390.10 259741.50

Plug load related energy use (EP) (kWh/m2 :yr)

Lighting load related energy use (EL) (kWh/m2 :yr)

Total electrical load (EPL) (kWh/yr)

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Fig. 8. Distribution between occupied and unoccupied hour lighting load.

Fig. 9. Distribution between occupied and unoccupied hour plug load.

0.71 for open office. The trend line indicates that with an increase in occupancy, K decreases in the form of a power function. The deviation of data from trend line is higher for lower occupancy, which indicates high variability in energy use pattern during the low occupancy hours. Although the trend of K is similar (power function) for all space types, there is a significant difference in the slopes of trend lines indicating space wise variability in energy use. Additionally, there could be several other parameters which influence occupant-related energy use inside any building space [14]. Identification of these parameters is important to further explain the association between energy use and occupancy. So, in the next step, all possible parameters which could influence K, are grouped into two main categories: a) Occupant-related; and b) Others (Fig. 10). The parameters influencing energy use related to occupant behaviour are: 1) Occupant presence which indicates passive influence of occupant on K; and 2) Energy use cluster (EUC) which indicates active influence of occupant on K. Other parameters used in this study are: 3) Space type; 4) Class/No Class status; 5) Semester/No-semester status; 6) Day type; and 7) Time type. EUC is a key parameter which signifies active behaviour of occupants. It is identified by performing clustering analysis. At the same level of occupancy, possible energy use clusters are identified using K-means (here K: number of clusters) clustering algorithm. Fig. 11 and Fig. 12 represent a single dimensional clustering of plug and lighting loads, where y-axis is the range of clustered data and

x-axis is the number of instances (data points) used to perform this clustering. To identify the number of clusters, the popular ‘elbow method’ is adopted. In this method, clustering is performed with different values of K (for example, from 1 to 10) and for each cluster, the sum of squared errors (SSE) is calculated. SSE is the sum of the squares of the difference between values of cluster elements and cluster centroid. A lower value of SSE indicates better cluster division and is inversely proportional to the number of clusters. An ‘elbow’ point in the line plot of SSE and number of clusters is selected as the optimal number of clusters. The optimized number obtained for the plug and lighting load cluster in this study is K ¼ 4. These cluster levels (1e4) of EUC are used as indicators to represent the energy use behaviour by a group of occupants. Additionally, while occupancy is a quantitative parameter, the remaining are categorical parameters. To use these categorical parameters in a mathematical model, a unique value (i.e. Index) is assigned (see Table 3). Values of these parameters are assigned between 0 and 1 to match the scale of normalised occupancy. For example, the index for EUC is assigned between 0 and 1. Here, 0.25, 0.5, 0.75 and 1 are representative of cluster 1,2,3 and 4 respectively. To avoid multicollinearity issue among parameters, a correlation matrix is prepared before looking into the impact of the influencing parameters on K. It has been found that there is no strong correlation between any two parameters.

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Fig. 10. List of parameters which could affect energy use per person.

Fig. 11. Energy use clusters for plug load.

3.5. Parameter selection and model development for K

KPL and KLL ¼ f ðp1; p2; p3; p4; p5; p6; p7Þ

(11)

All possible parameters listed in Table 3 and Eq. (11) are based on literature and it cannot be anticipated with surety that all of these parameters can be used as inputs for prediction models. It will be important to evaluate whether the parameters stated have significant influence over K. Four steps of parameter selection process (separate for plug load and lighting load) used in this study for the model development are: a) Identification of all unique possible combinations of the 7 indicators, b) Building MNLR and DNN models for each combination with k-fold validation (5-fold in this study). c) Computation of average R2 and MAPE values for 5fold model corresponding to a combination of parameters and d) Identification of combinations with the highest R2 and lowest MAPE. The R2 and MAPE values obtained, corresponding to the overall best parameter group for each model type, are presented in Tables 4

and 5. The values presented in the aforesaid table are for the testing dataset as emphasis is given to develop a general model that can be used to predict K. It has been found that not every parameter is required for K prediction model and the required parameters vary with modelling methods. Overall, DNN based model is able to predict K with less parameters as inputs with a slightly better accuracy than MNLR model. However, both MNLR (with R2 0.89 to 0.97) and DNN (with R2 0.97 to 0.99) values show good prediction accuracy. For K-Plug load, Occupancy, EUC, space type, class status and Time type are the best input parameters in MNLR model with R2 of 0.89. DNN model requires one less parameter (Time type is not required) with much better R2 of 0.97. For both models, semester and day information parameters are not adding much value in predicting K-Plug load. The error (MAPE) between actual and DNN model predicted is lower (1.3%) as compared to 4.99% of MNLR predicted. For K-Lighting load, Occupancy, EUC, space type and class are the best input parameters for MNLR model with prediction accuracy R2 of 0.97. DNN model does not require class status information and prediction accuracy is slightly better compared to MNLR

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Fig. 12. Energy use clusters for lighting load.

Table 3 Parameters which could influence K. Parameter ID

Parameter

Value

p1 p2

Occupancy Energy Use cluster

p3

Space type

p4

Class/No Class status

P5 p6

Time type Semester/No semester type

p7

Day type

0e1 0.25 e (lower values - Cluster 1) 0.50 e (2nd lowest values - Cluster 2) 0.75 e (2nd highest values - Cluster 3) 1.00 e Class 4 (highest values - Cluster 4) 0.25 e Classroom 0.50 e Open office 0.75 e Computer room 1.00 e Closed office 0.50 e Class hours 1.0 e No class hours 0-1 (where 1 means 24:00 h) 0.50 e Semester hours 1.0 e No semester hours 0.14 - Monday 0.29 e Tuesday 0.43 e Wednesday 0.57 e Thursday 0.71 e Friday 0.86 e Saturday 01 - Sunday

Table 4 Model for K-Plug load. Model type

No of indicators

Possible combinations

Best combinations*

R2 for best combinations

MAPE for best combinations

MNLR DNN

5 4

21 35

p1-p2-p3-p4-p5 p1-p2-p4-p3

0.89 0.97

4.99% 1.3%

Table 5 Model for K-Lighting load. Model type

No of indicators

Possible combinations

Best combinations*

R2 for best combinations

MAPE for best combinations

MNLR DNN

4 3

35 35

p1-p2-p3-p4 p1-p2-p3

0.97 0.99

9.25% 4.63%

*(p1-Occupancy, p2-EUC, p3-Space type, p4-Class/No class, p5-Time type, p6-Semester status, and p7-Day type).

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(R2 of 0.99). For both models, semester type, time and day information are not adding much value in predicting K-Lighting load. Similar to K-plug load models, error (RMSE) between actual and DNN models predicted is lower (4.63%) as compared to 9.25% of MNLR predicted. Although the best combination of parameters is identified for K plug and lighting load prediction models, it is important to measure the influence of each selected parameter individually on prediction accuracy to eliminate less important parameters. It can be seen from Fig. 13 that occupancy and EUC are the two prime parameters to predict K-plug and K-lighting. In the case of lighting-load, R2 has increased from 0.74 to 0.91 when EUC is added with occupancy. Similarly, for plug-load, R2 has increased from 0.57 to 0.89. The increase in correlation by adding EUC with occupancy is higher for plug load. This indicates that the occupants have a greater active influence on plug load compared to lighting load. The estimated MNLR-based K prediction model in two forms, one with all parameters (Eq. (12) and (13)) and one with best combination of parameters (Eq. (14) and (15)) are now presented. MNLR model with all parameters:

correlation has reduced slightly for MNLR (0.97e0.90) and DNN (0.99 to o.96). MAPE obtained for plug load are 3.15% and 2.37% between actual and predicted values for MNLR and DNN respectively. Similarly, for lighting load, MAPE obtained are 10.24% and 9.67% between actual and predicted values for MNLR and DNN respectively. Further, Fig. 15 through 20 compare the actual and model predicted K values at various occupancy levels for classroom, open office and computer room individually. Overall, the highest K prediction accuracy (R2 up to 0.99) for plug load is found for classrooms using DNN model, whereas, the lowest K prediction accuracy (R2 as low as 0.76) for plug load is found for open office using MNLR model. The highest K-plug load prediction accuracy for classroom could be due to the fact that the plug load of these spaces is mainly the projector and an associated desktop which operates during class hours only. However, the K-plug load of offices are mainly individual level desktop and printers for the open office, which is commonly ON even without the presence of occupants. Further, the highest K prediction accuracy (R2 up to 0.99) for lighting loads is found for open office using DNN model, whereas, the lowest K

  KPL ¼ 0:4978  p10:96959  p20:31558  p30:042629  p40:036428  p50:010551  p60:002358  p70:0096554

(12)

  KLL ¼ 0:8661  p10:99007  p21:0862  p30:33794  p40:019421  p50:0069022  p60:00021901  p70:00036568

(13)

  KPL ¼ 0:47494  p10:97769  p20:31786  p30:046262  p40:041465  p50:0088721

(14)

  KLL ¼ 0:86554  p10:99016  p21:106  p30:334843  p40:019783

(15)

MNLR model with best combination of parameters: 3.6. Validation of K prediction model Performance of the K model is validated with a validation dataset. As discussed in the methodology section, validation data is reserved data from similar spaces of the floor studied that has not been used for the model development. The required best combination of input parameters for models are normalised or constructed in form of model input requirements (i.e value between 0 and 1) as it was done in model development process. The validation is performed in three steps to check whether the model can be used in different locations for similar spaces: a) Comparison the estimated and actual K b) Use of K prediction model to calculate space wise energy use and c) Use of K prediction model to quantify space wise possible energy saving.

prediction accuracy (R2 as low as 0.81) for lighting loads is found for classrooms using MNLR model. The slightly higher prediction accuracy for K-open office lighting load compared to K-classroom and K-computer room lighting load could be due the fact that the open office is mostly illuminated during occupied hours whereas the classroom and computer room lights are turned on based on the operation of projectors.

3.6.2. Use of K prediction model to calculate space wise energy use This subsection of validation discusses the use of K prediction model to calculate the plug and lighting load energy. The calculation requires mainly two steps: a) Estimation of K values using K prediction models and b) Calculation of plug and lighting loads using predicted K values as inputs for Eqs. (16) and (17).

Plug load ¼

n X

Normalized Occupancyi  KPLi

(16)

i¼1

3.6.1. Comparison between predicted and actual K EUC as an input parameter for Eqs. (14) and (15), is prepared using validation data set i.e by performing k-means clustering. Similar to the case of testing dataset, it can be seen from Fig. 14 that the DNN model performs slightly better in predicting K at lower occupancy (with high variation in energy use) as compared to MNLR model, both for plug and lighting load. R2 between actual and predicted plug load values has reduced slightly for MNLR (0.89e0.84) and DNN (0.97e0.93). Similarly, for lighting load,

Lighting load ¼

n X

Normalized Occupancy i  KLLi

(17)

i¼1

here, KPLi is K model for plug load. KLLi is K model for lighting load. n is no of data points. To calculate the predicted values of plug and lighting loads at

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loads for different spaces are presented below in the form of a boxplot: Fig. 21a, b and 21c show the range of actual and predicted energy use values for classroom, office and computer room. Overall, the mean values for DNN predicted plug and lighting load are slightly closer to actual energy use as compared to MNLR values. When the total plug load and lighting load for the entire dataset is calculated using Eqs. (16) and (17), DNN and MNLR model predict lesser energy use compared to actual consumption for both plug and lighting loads (See Table 6). Overall, the performances of MNLR and DNN algorithms are comparable. The advantages of MNLR model are that it is less complex compared to DNN and its model structure is known, hence it can be used directly for the similar kind of spaces. Whereas, the advantage of DNN model is that It is more accurate with less input parameters.

Fig. 13. Selection of best parameter combinations.

each occupancy data point, the value of occupancy is simply multiplied with the predicted values of K obtained in the previous section. The calculated actual predicted values of plug and lighting

3.6.3. Summary of proposed K prediction and energy use calculation method Based on the analysis discussed in previous section, Fig. 22 shows a generalized methodology of K prediction and its potential use for plug and lighting energy calculation. The proposed method has mainly four steps. In the first step, a time-series data of occupancy, plug load and lighting load can be obtained from a space similar to the one studied, using occupancy sensors and energy meters. In addition to the occupancy data, other information such as space type, “class/No class” status and time type can also be obtained for a similar duration. In the second step, while occupancy data can be directly processed into a normalised occupancy scale between 0 and 1, the energy use data is first assigned a EUC (cluster value) using k-means clustering (k ¼ 4) followed by converting the EUC into the range of 0e1 as per Table 3. In the third step, the proposed K prediction model as in Eqs. (14) and (15) can be used to

Fig. 14. Actual vs predicted energy use per person with a validation dataset.

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Fig. 15. Actual and predicted K for classroom plug loads.

Fig. 16. Actual and predicted K for classroom lighting loads.

Fig. 17. Actual and predicted K for open office plug loads.

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Fig. 18. Actual and predicted K for open office lighting loads.

Fig. 19. Actual and predicted K for computer room plug loads.

predict K values. Finally, in the fourth step, Eqs. (16) and (17) can be used to predict energy use. The robustness of the proposed models can be seen by observing the MAPE and R2 between actual and predicted values. If the proposed model is found robust, the facility manager can effectively quantify the energy savings by replacing actual EUC with intended EUC, based on energy saving measures. For example, the actual lighting load EUC value could be 1 even for an open office space with very low (say 25% of designed) occupancy, meaning that even the spaces with no occupancy are illuminated. In this case, the facility manager can replace the value of EUC with 0.25 to see the amount of energy savings. Additionally, if there is an availability of predicted occupancy, the facility manager can use it along with an intended occupancy and EUC relationship to predict an efficiently operating plug and lighting energy system. This means that the potential use of the model proposed in this study would be to predict or quantify energy use if the occupants are encouraged to save energy by modifying their behaviour.

3.6.4. Use of K prediction model to quantify possible energy saving In this section K prediction model is used to identify possible energy savings if the occupants are encouraged to save energy by modifying their behaviour. This is important as the calculated plug and lighting load energy in previous section is based on existing energy use behaviour of spaces where the energy use cluster has no direct correlation with occupancy. With existing behaviour, same amount of energy use is dispersed along the various occupancy levels. This indicates a possible energy wastage for both plug and lighting load during occupied hours. Suggested operational rule for Plug load savings: In a relevant study, it has been identified that plug load typically shows a strong correlation (R2 up to 0.80) with occupancy [41]. However, the current study has observed a poor correlation (R2 up to 0.28) between occupancy and plug load, which could be an indication of energy wastage. The spaces studied, such as offices, classrooms and computer rooms do not have maximum occupancy at all times. However, it has been observed that their plug loads are maintained at full occupancy levels irrespective of the actual occupant count.

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Fig. 20. Actual and predicted K for computer room lighting loads.

Fig. 21. Box plot of actual and predicted energy use.

The difficulty in operating the energy use systems at varying occupancy levels could be a leading cause of energy wastage. For example, in the computer room and open office, the computers and printers are left in “switch on” mode by a few occupants in their

absence. It is also possible that many of these computers are actively running at full load even when there is no occupancy. It is common to have simulation programs and computational software running at most times. In addition, the lighting system has no

P. Anand et al. / Renewable Energy 143 (2019) 1143e1161 Table 6 DNN and MNLR predicted total energy use vs actual consumption. Energy use type

Plug load Lighting load

Model type

DNN MNLR DNN MNLR

Reduction in predicted energy use as compared to actual consumption Classroom

Open office

Computer room

5.2% 9.8% 2.5% 6.8%

18% 36% 1.4% 3.6%

2.5% 6.1% 0.8% 2.2%

control with respect to occupancy. Identification of actual energy savings requires plug load data for the group as well as the individual occupant level. With the existing dataset in this study, possible energy savings are estimated assuming an ideal plug load operational scenario i.e. if occupancy range changes, plug load range will also change [41]. Additionally, there is the fact that there could be variation in plug load usage by different occupants, and there can also be some common devices such as printers. Considering this energy use variability, the rule for plug load operational scenario is taken for occupancy ranges of 0e25%, 25%e50%, 50%e 75% and 75%e100%, plug load would fall anywhere in cluster 1, 2, 3 and 4 respectively. For example, if a computer room has 20 computers and a maximum occupancy of 20 and if there are only 5 (25%) persons inside, a maximum of 5 (25%) computers should be in operation and if there are 10 (50%) persons inside the space, then a maximum of 10 (50%) computers should be in operation. Suggested operational rule for Lighting load saving: Switching on all lightings of classroom, computer room and open office at low occupancy levels could be considered an inefficient lighting load operation. The classroom, computer room and open office (size 11.6  6.2 sqm, 11.6  10.5 sqm and 25.7  8 sqm respectively) have multiple illumination sources (tube lightings) with a gap of 2 feet between two tube lightings. Because multiple light sources are uniformly placed in the space, there is a possibility of reducing the lighting load by limiting the use of space according to occupancy. This can be done by dividing the room into 4 illumination zones from front to back (as shown in Fig. 23) and controlling the lightings zone wise as per occupancy level. The rule is to make the occupants fill the space zone wise so that the entire space does not consume all lighting load energy at lower occupancy levels. Similar to plug load, the lighting load could fall anywhere in clusters 1, 2, 3 and 4 for occupancy ranges of 0e25%, 25%e50%, 50%e75% and 75%e100% respectively.

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To identify the possible energy savings for plug and lighting load for various spaces, validation dataset and revised EUC are used as inputs for Eqs. (16) and (17). Both MNLR and DNN models can be used to calculate possible energy savings, however DNN model is preferred because it gives better accuracy. Fig. 24 shows the box plot for the normalised actual and suggested rule (behaviour) based plug and lighting energy use for different spaces. Further, this normalised data is converted into its actual units and represented in the form of cumulative energy use for each space individually, as in Fig. 25. The calculated differences between this ruled-based plug load energy use and actual behaviour-based plug load are 8.9% for classroom, 3.1% for open office and 1.3% for computer room. Similarly, for lighting load, the differences are 65.1% for classroom, 43.6% for open office and 38.4% for computer room. These differences could be possible energy savings, by adopting a rule-based operation of lighting and plug load using sensors/actuators, as suggested in this study.

4. Discussion There are several reported cases of occupancy-based lighting control; for example, a study has investigated the energy savings from 56 offices and 72 classrooms by installing Passive Infrared (PIR) and ultrasonic based occupancy sensors compared to manual control and identified up to 19% of energy saving possibilities for office buildings and 11% for schools [42]. Similarly, a study has reported 20e26% of energy savings by occupancy-based lighting control [43]. Another researcher has reported up to 43% energy saving by implementing occupancy-based lighting control using PIR based sensors [44]. A study has developed a model to predict energy savings based on occupancy and found that 33.3% lighting energy savings are possible in an office building [17]. These reported energy savings are as high as 75% for lighting loads in the recent years [41,45]. This study compares the energy use by different kind of zones such as classroom, computer room, open office and closed office in a floor of an institutional building and develops energy use per person model for each space. The primary contribution of this study lies in the scale of its use and its exploration of possible decisionmaking tools to control the building energy use up to zone level. In earlier studies, the data sampling period was short (a day, or up to a week); however, in this study, an extensive 90-day period of actual building operational data has been gathered from individual zones for analysis. This study is in line with a study by Kim (2017), in which a simple linear relationship between occupancy and

Fig. 22. K prediction and energy use calculation method.

P. Anand et al. / Renewable Energy 143 (2019) 1143e1161

EUC 1 for Occupancy <=25%

Illumination Zone 1

Illumination Zone 1

EUC 3 for 50%
Illumination Zone 1

EUC 2 for 25%
Illumination Illumination Zone 2 Zone 3

Illumination Zone 2

EUC 4 for 75%
Illumination Zone 1

Illumination Illumination Illumination Zone 2 Zone 3 Zone 4

11.6 m Fig. 23. Rule based operational strategy for lighting loads.

Fig. 24. Box plot of actual and suggested behaviour-based energy use.

6.2 m

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Fig. 25. Total actual and suggested behaviour-based energy use.

energy was observed and it was found that occupancy is highly correlated with total electrical energy use. Additionally, it was also found that the correlation is even stronger with plug-load individually (up to 80%) for institutional building space [41]. In comparison with the findings of Kim (2017), this study found a much lower correlation (less than 24%) between occupancy and energy use even for the plug loads, hence the occupancy and energy use cannot be linearly modelled for the space studied. However, it has also been observed that with an increase in occupancy, energy use per person decreases nonlinearly. The correlation between occupancy and energy use per person has been seen as high as 0.87 and it varies from space to space. Based on these findings, a new approach has been developed in this study to model energy use per person as a function of occupancy for the first time for different kind of spaces using both deterministic (multiple nonlinear regression) and stochastic (deep neural network) algorithms. Other than modelling energy use per person stochastically, the novelty of this study also lies in a robust iterative process to study the impact of various parameters such as day type, time type, class/no class and occupants’ energy use behaviour on energy use per person. Finally, this study has identified energy saving possibilities for different zones during both occupied and unoccupied hours. The energy saving during unoccupied hours is identified by discontinuing the use of plug and lighting loads during unoccupied hours; whereas the energy saving during occupied hours is obtained by using the energy use per person model of plug and lighting loads based on suggested energy use behaviour. The expected plug loads energy savings is up to 12% for computer room, observed to be highest; whereas the expected lighting load energy savings is up to 59% for the circulation spaces, also observed to be highest during unoccupied hours. Subsequently, with the suggested energy use behaviour, the expected plug load energy savings are 8.9% for classroom, 3.1% for open office and 1.3% for computer room; whereas the expected lighting load energy savings are 65.1% for classroom, 43.6% for open office and 38.4% for computer room during occupied hours. This study is the first to use energy use per person as a dependent parameter to explain both plug and lighting energy use. 5. Limitation of study In this study, possible energy savings are identified using a rulebased plug and lighting load related energy use calculations. However, these rules are based on an assumption that EUC has a

strong correlation with occupancy to save energy. Additionally, these savings depend on the effectiveness of the control system which controls the plug and lighting loads in real-time. Several studies in the past have reported energy savings with occupancybased plug and lighting energy usage. However, it has also been stated that the delay setting of sensors, i.e. the time to switch off the light after the detection of the last occupant, play a vital role in affecting the amount of energy savings [46]. This study is conducted considering that the occupancy-based lighting and plug load control systems are operating ideally (zero-time delay of sensors) and can vary in real-time operation based on sensors’ settings and performances. Further, to use the model developed in this study, the numerical value of each categorical parameter must be assigned equal to that used in this study. Additionally, to investigate this model for other spaces, occupancy and EUC are required, and occupancy counting, and energy monitoring infrastructure needs to be available. The model would be more accurate for spaces which have energy use behaviour similar to that identified in this study. 6. Conclusions This study systematically investigates the relationship of occupancy with plug and lighting load energy consumptions in an institutional building. An energy use per person model is developed to explain the stochastic relationship between energy and occupancy. The key conclusions from this study are the following:  Firstly, it is observed that the potential of energy saving during unoccupied hours for plug load related energy savings is up to 12% for computer room; whereas the expected lighting load energy savings is up to 59% for the circulation spaces.  Subsequently, the potential of energy saving during occupied hours with the suggested energy use behaviour for plug load are 8.9% for classroom, 3.1% for open office and 1.3% for computer room; whereas the expected lighting load energy savings are 65.1% for classroom, 43.6% for open office and 38.4% for computer room during occupied hours.  During occupied hours, a high variability in energy use within similar occupancy levels indicated a stochastic relationship between energy use and occupancy. However, Energy use per person (K) shows a significant correlation with occupancy through a power function relationship. This indicated that occupancy and energy use can be modelled nonlinearly.  Prediction model of K as a function of occupancy has been developed using time series data of the spaces studied. The

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general usability of the model developed has been demonstrated through its potential use for the plug and lighting load estimation and possible energy saving calculations. Overall, the model estimates highest energy saving for spaces which have least correlation between occupancy and K. The primary outcome of this study is the K model development methodology, which can be further explored for other space types of an institutional building, which have high variation in occupancy during operational hours, such as lecture theatre, laboratory, studios, etc. The data required to develop this model consists of metered plug load and lighting load data and occupancy data which can be obtained using existing technologies such as camera, wi-fi, infrared, CO2 etc. In real building scenarios, if there is an availability of future occupancy, facility managers can use the model to predict an efficiently operated plug and lighting energy use based on the intended relationship between occupancy and EUC. The performance of the model developed can also be verified from similar spaces with different floor areas, in different geographical locations.

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