Modeling and analysis of the electricity consumption profile of the residential sector in Spain

Journal Pre-proof

MODELING AND ANALYSIS OF THE ELECTRICITY CONSUMPTION PROFILE OF THE RESIDENTIAL SECTOR IN SPAIN. ´ P. Escobar , E. Mart´ınez , J.C. Saenz-D´ıez , E. Jimenez , J. Blanco PII: DOI: Reference:

S0378-7788(19)31047-3 https://doi.org/10.1016/j.enbuild.2019.109629 ENB 109629

To appear in:

Energy & Buildings

Received date: Revised date: Accepted date:

7 April 2019 10 October 2019 24 November 2019

´ Please cite this article as: P. Escobar , E. Mart´ınez , J.C. Saenz-D´ıez , E. Jimenez , J. Blanco , MODELING AND ANALYSIS OF THE ELECTRICITY CONSUMPTION PROFILE OF THE RESIDENTIAL SECTOR IN SPAIN., Energy & Buildings (2019), doi: https://doi.org/10.1016/j.enbuild.2019.109629

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.

MODELING AND ANALYSIS OF THE ELECTRICITY CONSUMPTION PROFILE OF THE RESIDENTIAL SECTOR IN SPAIN. P. Escobara, E. Martínezb1, J.C. Saenz-Díezc, E. Jiménezd, J. Blancoe a

Department of Mechanical Engineering, University of La Rioja, Edificio

Departamental - C/ San José de Calasanz, 31 - 26004 Logroño, La Rioja, Spain. Phone:(+34) 941 299 524. Email: [email protected] b1

Department of Mechanical Engineering, University of La Rioja, Edificio

Departamental - C/ San José de Calasanz, 31 - 26004 Logroño, La Rioja, Spain. Phone:(+34) 941 299 524. Email: [email protected] c

Department of Electrical Engineering, University of La Rioja, Edificio Departamental

- C/ San José de Calasanz, 31 - 26004 Logroño, La Rioja, Spain. Phone:(+34) 941 299 502. Email: [email protected] d

Department of Electrical Engineering, University of La Rioja, Edificio Departamental

- C/ San José de Calasanz, 31 - 26004 Logroño, La Rioja, Spain. Phone:(+34) 941 299 502. Email: [email protected] e

Department of Mechanical Engineering, University of La Rioja, Edificio

Departamental - C/ San José de Calasanz, 31 - 26004 Logroño, La Rioja, Spain. Phone:(+34) 941 299 524. Email: [email protected]

1

Corresponding author. Department of Mechanical Engineering, University of La Rioja, Edificio Departamental - C/ San José de Calasanz, 31 - 26004 Logroño, La Rioja, Spain. Phone: (+34) 941 299 524. Email: [email protected] 1

MODELING AND ANALYSIS OF THE ELECTRICITY CONSUMPTION PROFILE OF THE RESIDENTIAL SECTOR IN SPAIN Abstract The determination of electricity consumption profiles in the domestic sector is a very complicated task due to the variability of the consumer. This sector covers a wide variety of sizes and types of consumers; it has, as well, a wide variability in the occupancy of homes, and therefore, the measurement of final consumption has a very high cost. In this article a new bottom-up stochastic simulation model is presented, nourished by data provided by the Survey of Time Employment of the National Institute of Statistics of Spain (INE). The algorithm permits an estimation of the average profile of regular electricity consumption in Spain according to the number of members of the house and the day of the week. Unlike some previous research, the average profile is studied, and all household uses are separated. These results are the basis of a line of research on self-consumption, but they are also useful as the basis for many other studies on energy consumption, energy efficiency, demand management, hourly rates, energy policies, etc. Keywords: Electricity Consumption; Electricity Demand; Residential Sector; Survey of Time Employment.

1. INTRODUCTION The residential sector constitutes a key sector for the current energy network and power grid in the European Union. Power consumption in this sector amounts to 29% of total consumption. In Spain, this percentage is a little lower, standing at 25%. This figure and a shift towards an upward trend are due mainly to the increase in the number of

2

households as well as in appliances and the comfort they provide [1]. Electricity consumption in households in the European Union has also increased by 2% annually in the last 10 years, despite the fact that a great progress has been made in household appliances and light energy efficiency. This is due to the growing number of information and communication technologies (ICT) and facilities and electronic devices. In this type of technology, stand-by power consumption plays a very interesting role and offers considerable savings potential. The domestic sector in the countries of the European Union is subject to a high impact of consumption during periods of peak demand throughout the day. This can lead to saturations in the electricity network with a consequent increase in generation and investment costs for the reinforcement of the network [2]. The domestic sector presents some significant newly established advances, although not in a generalized way. For many years, the need to know the electrical consumption of the different household appliances has been vital to make a good forecast of consumption [3], increase the use of more efficient equipment to reduce consumption in an important way [4], and expand the knowledge of new strategies of demand side management (DSM) in micro-generation and electrical network [5, 6]. Based on models, it is estimated that the potential savings in electricity consumption in the residential sector can reach a 48% by implementing existing technologies and improving energy saving habits [7]. In the case of DSM, the capacity of the time shift of electricity consumption is very important, by moving forward or delaying the use of domestic appliances. In addition, the use of heat pumps and the charging of electric cars can be controlled. All of these can be linked to a control of costs of electrical energy.

3

Domestic micro-generation for self-consumption alters the profile of domestic consumption from the point of view of the company supplying electricity. Gautier et al. [8] studied the factors that encourage self-consumption and one of the most important is to provide information to users in relation to their consumption. 1.1. Determination of household power consumption profiles The determination of electricity consumption profiles in the domestic sector [9, 10] is a very complicated task due to the variability of the consumer [11]. This sector covers a wide variety of sizes and types of consumers; it has, as well, a wide variability in the occupancy of homes [12], and therefore, the measurement of final consumption has a very high cost [13, 14] because it is necessary to install electric energy analyzers in each consumer. Other sectors, such as commercial and industrial ones, are better characterized because the property is more centralized, there is greater interest in reducing energy consumption, and more documentation is available. For the modeling of energy consumption in the residential sector there are two welldifferentiated techniques, which are top-down and bottom-up [15, 16]. Top-down is a method that considers the residential sector as an energy receiver but does not take direct account of individual end-uses. It takes into account the history of aggregate energy values and calculates the consumption of a group of houses based on high-level variables such as macroeconomic indicators (unemployment, inflation, and gross domestic product), the price of energy and the general climate. Khan et. al. [17] introduced the time-segmented regression analysis (TSRA) to identify the factors (household age, monthly income, energy saving behavior, etc.) that determine demand at different segments across the day in New Zeland.

4

Bottom-up models extrapolate the estimated energy consumption from individual home or end-use appliances over the regional or national level [18, 19]. The direct measurement of the final energy consumption in each household appliance [20] is very suitable for the generation of electricity consumption profiles at homes [21]; although, it is complex and expensive, so resorting to the aforementioned bottom-up models may be the more viable option. Accurate results may be obtained with a high temporal resolution with bottom-up models [22]. In addition, these models can assess the impact of energy saving measures such as changing the operating hours of household appliances. They are not complex models but they require the availability of an extensive database [23]. The bottom-up models are based on the individual end-uses and successively these consumptions are aggregated to reach broader levels [22]. Wen, L, et al [24] proposed a K-means algorithm to reduce the dimensions of smart meter time series of 3.000 daily electricity consumption profiles of 1.000 residents and the shape-based clustering method can effectively find similar shapes and identify typical electricity consumption patterns. Motlagh et. al. [25] suggest the clustering of electricity customers supports effective market segmentation. In the bottom-up models the consumption in the houses is determined through the characteristics of the house and type of household appliances, as well as the behavior of its occupants. Among all of these information, active occupation data (that is, when household members are at home but not asleep) is very important because it is closely related to the profile of electricity demand. When people are at home and are not asleep, each activity is associated with a large number of appliances. In recent years several

5

researchers have used data taken from time-use surveys (TUS) to establish a relationship between human behavior and the use of electric power. Widen et al. [22] developed a model for the calculation of daily electricity and hot water demand, whose aggregate values resembled the data measured in the most recent studies in Swedish households. The deviations were mainly due to poorly collected time-use data and an aggregate population level that was not a representative sample. Likewise, a model based on non-homogeneous Markov chains that was adjusted to a set of empirical time-use data was designed, analyzing a set of activities with a resolution of one minute [26]. The model generated by Richardson et al. [27] had a resolution of 1 minute in the simulation of the use of household appliances. To validate the model, data from 22 households in the East Midlands (UK) were collected for 1 year. The qualitative comparison between the synthetic and the measured data showed that they have similar characteristics. Lopez-Rodriguez et al. [1] analyzed the data of the Employment Survey of Time in Spain and extracted information in relation to the active occupation of Spanish households. The profile of electricity consumption in the residential sector was highly related to the time of active occupation of homes. Three peaks of active occupation were identified, which coincided with morning, noon and afternoon. Based on this information, a stochastic model was created that generated active occupation profiles, with the aim of simulating domestic electricity consumption. The implemented models considered the houses with different number of occupants, an aspect that is important to account for the distribution of appliances, lights, and temperature conditioning (heating or air conditioning). In addition, they distinguished

6

the weekdays from weekends. After obtaining the profiles of active occupation, the maximum occupancy peaks were identified, and the activities of the occupants associated with the use of the appliances were determined in those periods of time. In addition, the occupation variation was analyzed to identify strategies to follow in order to reduce consumption in average or its peaks. In 2011, Chiu et al. [28] developed a high resolution spatial model based on data from the US Employment Survey. The model was based on a theoretical framework that explained the physical characteristics of the home, the nature of energy use behaviors and the way in which housing and its occupants interact. Experiments were carried out with homes of 3 to 5 people to examine the properties of the model. The results indicate that the load profiles of the dwellings with the same composition have a very narrow range of variation and have a lot of similarity with the measured data. Likewise, an algorithm of extreme complexity was designed for the simulation of occupancy, which was used as input for behavioral models within building simulation tools [29]. In this article a new stochastic simulation model is presented, nourished by data provided by the Survey of Time Employment of the National Institute of Statistics of Spain (INE). The algorithm permits an estimation of the average profile of regular electricity consumption in Spain according to the number of members of the house and the day of the week. Unlike some previous research, the average profile is studied and all household uses are separated. The objective of obtaining these electricity consumption profiles is to use them as a basis to study strategies of electric self-consumption with different types of homes, and days of the week. The results are extensible to all households in general departing from the typical profile considered. In addition, the independence of the results of the different electrical devices that can be used in the house facilitates the possibility of 7

proposing different scenarios of modification of consumption profiles, based on estimates of the evolution in the efficiency of household appliances or in the change of schedules of different activities throughout the day. It is also possible to study the time shift of the operation of household electrical appliances and in this way carry out an efficient management of the demand. Based on the results obtained, and with these strategies, simulation of reductions in the upward trend of electricity in the residential sector can be developed, as well as the impact on peak demand throughout the day. Consequently, the main objective of this article is to achieve an average profile of electricity consumption in Spain at present, as well as generate a series of profiles of electricity consumption differentiated by household appliances used, which serve as a basis to generate new profiles according to the evolution of consumption patterns. These results are the basis of a line of research on self-consumption of our Research Group, but they can also serve as the basis for many other studies on energy consumption, energy efficiency, demand management, hourly rates, etc. In the next section, a model is developed to estimate the average profile of regular electricity consumption in Spain according to the number of members of the house. The third section presents the results by applying the model with total consumption of household and consumption by appliance. The final section summarizes the most important conclusions. 2. METHODOLOGY The work methodology (Figure 1) for the creation of a model for the determination of the temporary electricity demand of Spanish households is based on a series of stages that are described below.

8

Figure 1 Model for determining the temporary demand for electricity This model bottom-up uses the appliance as a basic household block, where “appliance” refers to any individual domestic electric load, such as a television, washing machine or dishwasher (see Figure 2). The characteristics of electrical appliances (average power and cycle time) have been calculated and adjusted to fit the data published in the SECHSPAHOUSEC Final Project Report [32] of energy statistics in the residential sector.

9

Figure 2 Simplified visualization of the decision procedure used to obtain the temporary demand for electricity 2.1. Obtaining data for the study of Survey of Employment of Time The first source for obtaining data is the Survey of Employment of Time 2009-2010 (SET) of the National Institute of Statistics of Spain [30]. The Survey was conducted in 9,541 homes distributed throughout the year. The grid of time occupies 24 consecutive 10

hours (from 6:00 in the morning until 6:00 the next day) and is divided into 10-minute intervals. The micro data corresponding to the SET are used to know the occupation of each one of the dwellings and the activities that each resident performs every 10 minutes. Knowing the activities carried out helps to identify how many people are in the house and are not asleep, which can give an approximation of the electrical consumption of the house. The activities are codified according to a harmonized list of Eurostat [31], which considers 10 major groups: personal care, paid work, studies, home and family, voluntary work and meetings, social life and fun, sports and outdoor activities, hobbies and information technology, means of communication, journeys, and not specified use of time. In this way it is possible to obtain information about the percentage of people who perform an activity during the day, the average daily time (in hours and minutes) dedicated to an activity, the distribution of activities on an average day (week days or weekend) and the percentage of people who perform the same activity at the same time of day (daily activity rates).The use of appliances within a home is related to the number of people who are at home and awake. This is called active occupation, and is represented in each house within the model as an integer that varies throughout the day in a pseudo aleatoric manner, reflecting the habitual behavior of people in their daily lives. It seems to be random but it is totally deterministic. Active occupation is highly related to electricity demand. This survey includes a file with the characteristics of the households such as location, type of household, number of members, etc. In this model, the parameter of the number of members has been used to analyze its impact on electricity consumption. 2.2. Application of elevation factors

11

Through a routine, all the households’ data of the survey are read with their members and their factors of elevation of the micro data of the TUS. Any characteristic or estimate can be expressed in simplified form as: ∑

(1)

where the sum is extended to the scope to which the estimate refers, households or persons, xj is a variable that takes the value 1 or 0 according to whether the household or person possesses or not the characteristic investigated, and dj is the weight or elevation factor. 2.3. Characteristics of electrical appliances In this bottom up model, a good definition of the consumption of appliances is really important. To adjust the power and duration of the operation of the electrical appliances associated with the different activities, the data published in the SECH-SPAHOUSEC Final Project Report [32] are used. This report is included in the SECH project (Development of detailed Statistical on Energy Consumption in Households) proposed by Eurostat to member states, whose objective is the development of energy statistics in the residential sector through bottom-up methodologies of measurement and modeling. The data have been obtained through telephone surveys about electrical equipment and consumption in the residential sector, as well as in-person surveys on equipment, consumption and energy behavior in the residential sector and measurements of electrical consumption in 600 households. It is a report with a bottom-up methodology, which has obtained a very slight deviation in the values of electricity consumption (less than 5%) compared to the top-down methods, so this type of approach is considered adequate. The total electricity consumption in Spain is 59,983 GWh. Likewise, in the same report it is indicated that, of the aforementioned consumption, 37,068 GWh

12

(61.8%) corresponds to household appliances (refrigerators, freezers, washing machines, dishwashers, dryers, ovens, TV, computers and other equipment), 7,045 GWh (11.7%) corresponds to lights, and 5,572 GWh (9.3%) corresponds to the kitchen. The rest corresponds to heating, cooling and sanitary hot water, which are out of the object of this paper because they are very variable services in energy consumption. All the data of energy consumed by the different services that appear below were taken from the Final Report of the SPECH-SPAHOUSEC Project [32]. Table 1 shows all the data that has been taken from as the starting point for calculations have been included. Table 1 Energy consumed data of the different services

Refrigerators and freezers Washing machines Dryers Kitchen Ovens Dishwashers TV Computers Lights Stand-by

Total consumption (GWh)

Percentage appliances

on

Percentage on total consumption

13,586

36.7 %

22.6 %

4,391

11.8 %

7.3 %

1,241 5,572 3,061 2,265 4,517 2,751 7,045 3,969

3.4 % 8.3 % 6.1 % 12.2 % 7.4 % -

2.1 % 9.3 % 5.1 % 3.7 % 7.5 % 4.6 % 11.7 % 6.6 %

Equipment rate (% homes) 99.7 % Refrigerators 23.2 % Freezers 92.9 % (7,1 % washer dryers) 28.3 % 69.0 % 77.1 % 53.1 % 99.9 % 93.0 % -

Average annual consumption per home (kWh) 655 558 254 255 238 263 245 119 145 231

These data have been used as a starting point alongside with the data on the use of time, the average powers of each of the services have been calculated. A washing machine has a variety of washing programs with different consumption curves. An average cycle time of 2 hours and 20 minutes has been assumed [22] and the resulting average power is 1.056 kW. The dryer is an appliance with less presence in Spanish homes and therefore with less influence on residential consumption. Assuming an average duration of three and a half hours [22], the average power calculated is 0.738 kW.

13

The kitchen and the electric oven are very present in households and their influence on total consumption is very important with an average power of the kitchen and oven 1.018 kW. The percentage of households that have a dishwasher slightly exceeds 50%. The estimated duration of each cycle is 2 hours and 20 minutes, with an average power per cycle of 0.082 kW. The total energy consumed by TVs in the Spanish electricity system is very high because it is a household appliance with many hours of use. Almost all the houses have at least 1 television and the average equipment rate is 2.2 TVs per home. After the results of the simulation, the average power consumption of a TV set can be considered as of 0.19955 kW. Like TVs, computers are some equipment present in 93% of homes, with an equipment rate of 1.2 per home. The consumption of refrigerators and freezers is relatively flat although it is possible to see a greater variation of consumption during the day due to the greater use and to have more doors opening. The calculated average power due to these two services is 0.090 kW. The definition of the Standby mode according to the IEC62301 standard and its European transcription IEC62301 is the lowest energy consumption mode, which cannot be turned off by the user and should remain for an indefinite time when the appliance is connected to the power supply and used in accordance with the manufacturer's instructions. The standby power is the average power in the standby mode. Consumption on stand-by is greater at night. During the day it goes down progressively until it reaches a minimum at approximately nine o'clock at night.

14

Although this consumption is not intended for anything operational, its influence on electricity consumption is important, reaching over 5%, as can be seen in the previous table. This consumption supposes 0.633 kWh/day, with an average power of 26 W. Finally, the electricity consumption associated with lights depends mainly on the level of natural light and the activity of the residents of the dwelling. Richarson et al. [27] presented a model of domestic lights whose inputs are the two aforementioned factors. This model results in data with a resolution of 1 minute for a large number of homes. Widen et al. [22] generated a bottom-up model based on housing occupancy patterns and natural light data. A non-homogeneous Markov chain was used for the generation of occupancy patterns and, through a conversion, it is transformed to a demand for lights based on the level of natural light. The probabilities of the transition of the Markov chain were determined with the data of the Use of Time in households in Sweden; also, the parameters for the occupation-lighting conversion were adjusted taking as reference the load curves of some measurements made. It was pointed out that photovoltaic energy has a negative correlation with the energy consumed due to lights. The model presented in this article is much simpler and based on average values. The average annual hour of sunrise in Spain is around eight in the morning and the evening time is around 7:00 p.m. During the night hours, the estimated average light power is of 0.162 kW. In daytime hours, some reducing coefficients are applied so that a curve similar to that shown in the INDEL Project is obtained [33]. 2.4. Connecting activities with energy demand Next, all the activities developed by the people surveyed in each household every ten minutes are analyzed. From these activities, those that can suppose an electrical consumption are selected.

15

There are four different ways to describe the energy demand connected to the activities: - Power demand not defined by any activity. It is mainly due to the use of refrigerators and freezers and to the standby. The load, although it may fluctuate due to door refrigerators opening for example, can be considered constant. - Constant power during the activity. The power demanded is considered constant throughout the activity, which is a simplification. Among these activities are cooking, ironing, cleaning, watching TV, and use of audio and computer devices. It will be taken into account if the person is accompanied during the activity to see if one or several appliances are taken into account. - Constant power demand after the activity. This activity applies to dishwashers, washers and dryers. Although the power demanded by domestic appliances varies during the operating cycle, the power is considered to be constant. In this way, if in a time interval the activity of making the laundry takes place, in the following 14 intervals of 10 minutes a constant load will be considered. - Power demand dependent on time. This applies mainly to electric lights. This means that for there to be lighting consumption there must be active occupation in the house, but the intensity of illumination will also depend on the time of day. The model uses a list of appliances and characteristic services that can be found in a home. Each service is assigned an average power demand value [32]. Once the total demand for each of the services has been calculated, the calculation of the average power per household is made, according to the number of household members and the day of the week. 3 ASSUMPTIONS AND LIMITATIONS.

16

Homes with electric heating and sanitary hot water systems have not been considered. On the one hand, according to the SPECH-SPAHOUSEC Project [32], the houses that do not have an electric heating system represent approximately a 65% of Spanish homes and 71% in the case of the sanitary hot water. On the other hand, there is a great variability of consumption according to the type of heating and DHW (geothermal, aerothermal, heat pumps, thermal emitters, etc.) and operating conditions (operating hours, setpoint temperatures, etc.) In the same way, air conditioning systems have not been considered either. In this case, homes without air conditioning represent 49% of Spanish homes. The electricity consumption has been analyzed in various sizes and types of homes. The sizes considered have been families of one member, two, three, and four. These families would account for practically 91% of the total (see Table 2). The size of the household, expressed as the number of household members, is a variable with a significant impact on energy consumption which in 2010 in Spain reached the average of 2.7 people/household [32]. Table 2 Proportion of families according to number of members [32] Size 1 member 2 members 3 members 4 members ≥5 members

% 22% 27% 22% 20% 9%

The electrical consumption produced on weekdays and on weekends is analyzed. It will be analyzed if the daily energy consumed and the demand curve is affected. Appliances can be used by more than one occupant at the same time. As an example, if a second occupant arrives at a house when the first occupant is cooking, it is likely that

17

only a slight increase in demand will occur. The data on active occupation is the basis for the modeling to allow the sharing of household appliances: the probability of an appliance use is increased non-linearly with respect to the active number of occupants. For example, the simultaneous use of lights and a TV set could likely happen inside a house that has active occupants one winter afternoon. Again, the use of active occupation data within the model gives a basis for determining such correlation. 3.1. Characteristics of households and the profile of equipment included in the study To polish the temporal model of electricity demand, a second mechanism is used, based on the activities of the occupants. Time of use (TOU) data is again used to create profiles; however, in this case, they are activity profiles, which show, for example, that people tend to cook over lunchtime. Similarly, it is more common to watch television in the afternoon. The next step is to join these activities to the appliances (watching television requires a TV set in use, cooking requires an oven, and a laundry needs a washing machine...). Assigning an activity profile to each appliance of the model, the variation of probability of that an appliance is used throughout the day can be taken into account in a stochastic simulation, which is the crucial element of the model presented in this paper. An appliance in a home can be considered as an individual domestic electric load. The use of the refrigerator and freezer does not depend on the active occupation. However, the use of a large number of appliances depends on the members who are active inside the home. They have been classified as moderately predictable, related to the habitual behavior of the occupants, or unpredictable, whose consumption of electricity follows a stochastic approach.

18

Firth et al. [34] conducted a study of electricity consumption monitoring in 72 UK households based on means every five minutes for 2 years. The conclusions of this study were that the form of housing is not important, being more important the number of occupants, number and type of appliances and patterns of active occupation. Gago et al. [35] developed a model that was able to calculate the electricity consumption of the residential sector associated with electric lights. These results were endorsed by questionnaires carried out on a group of households. This model was applied in Andalusia (Spain) and was aimed at replacing incandescent lamps with low consumption lamps within the Spanish Strategy for Climate Change and Clean Energy (SSCCCE). As a result, it was obtained that this policy could generate a reduction of 18% in the electricity bill and 0.61% in the total electricity consumption. 4. RESULTS AND DISCUSSION Applying the methodology of the previous section, a simulation model has been developed to estimate the average Spanish consumption profile according to the number of household members and the type of day (week or weekend). Throughout this article are presented all the results thrown by the model, including the profiles of consumption by services (washing machine, kitchen, refrigerators, etc. ...).

19

Figure 3 Average power consumption due to the use of washing machine in Spain Figure 3 shows the average hourly power in Spain of electricity consumption due to the use of a washing machine according to the number of household members. Between eleven and twelve in the morning the maximum occurs, and at six in the afternoon another peak of smaller magnitude is produced. It can be seen that there is an important difference between dwellings with 1 member and dwellings with 4 members, as indicated above. There are no clear differences between the weekend and midweek, with consumption in some cases being higher on weekends and vice versa in other cases. Table 3 shows the most important data.

Table 3 Average daily energy consumption calculated by use. Resident/household Average household 1 2 3 4 Average household 1 2 3 4 Average household 1 2 3 4 Average household 1 2 3 4 Average household 1 2 3

Daily average energy (kWh) % Average power washing machine per household 0.700 0.366 -47.70% 0.613 -12.37% 0.812 16.15% 0.895 27.92% Average power cooking per household 1.375 0.788 -42.68% 1.385 0.73% 1.448 5.32% 1.633 18.76% Average power dishwasher per household 0.358 0.197 -45.00% 0.365 2.07% 0.371 3.67% 0.431 20.50% Average power TV per household 0.720 0.573 -20.33% 0.777 7.91% 0.690 -4.14% 0.732 1.79% Average power PC per household 0.438 0.128 -70.72% 0.275 -37.26% 0.487 11.07%

N. of cicles/week 2,0 1,0 1,7 2,3 2,5

20

4 Average household 1 2 3 4 Average household 1 2 3 4 Average household 1 2 3 4

0.769 75.47% Average power lighting per household 1.122 0.853 -24.02% 1.088 -3.01% 1.172 4.45% 1.266 12.82% Average power other uses per household 1.122 0.853 -24.02% 1.088 -3.01% 1.172 4.45% 1.266 12.82% Average power per household 7.913 5.928 -25.08% 7.673 -3.04% 8.226 3.96% 9.025 14.05%

As shown in Figure 4, in Spain the mean hourly electricity consumption of energy due to the use of a dryer according to the number of household members is very similar to that of the washing machine, but displaced in time around 2 hours.

Figure 4 Average power consumption due to the use of clothes dryer in Spain Similarly, Figure 5 shows the average hourly power in Spain of electricity consumption due to the use of kitchen and oven according to the number of household members. The peak occurs about one-thirty in the afternoon, and about nine o'clock in the evening another peak of smaller proportion occurs, coinciding with lunch and dinner respectively. It can be seen that there is an important difference between homes with 1

21

member and homes with 4 members. During lunch, a greater consumption of electricity can be appreciated during the weekend compared to during weekdays. During dinner, there is a small increase in weekday consumption compared to the weekend. In summary, weekend and midweek consumption is practically similar (see Table 3).

Figure 5 Average power consumption due to the use of kitchen and oven in Spain Figure 6 shows the average hourly power in Spain of electricity consumption due to the use of dishwashers according to the number of household members. Between four and four thirty in the afternoon the maximum occurs, and about eleven at night another smaller peak occurs. It can be seen that there is an important difference between dwellings with 1 member and dwellings with 4 members. There are no clear differences between the weekend and midweek, with consumption in some cases being higher on weekends and in other cases consumption during the week (see Table 3).

22

Figure 6 Average power consumption due to the use of dishwashers in Spain. The average hourly power in Spain of electricity consumption due to the use of TV sets according to the number of household members is shown in Figure 7. The maximum is produced about 11 o'clock at night, and about 5 o'clock in the afternoon there is a peak but of smaller proportions. It can be seen that there is a difference between houses with 1 member and houses with 4 members, but not as important as other services, such as washing machines and dishwashers. There are clear differences between the weekend and midweek (see Table 3). The more residents the more energy consumption pattern cannot be observed.

Figure 7 Average power consumption due to the use of TV in Spain.

23

Figure 8 shows the average hourly power in Spain of electricity consumption due to the use of computers according to the number of household members. No peaks of consumption occur and therefore it is distributed throughout the day. It can be seen that there is an important difference between houses with 1 member and houses with 4 members. There are no clear differences between the weekend and midweek (see Table 3).

Figure 8 Average power consumption due to the use of computers in Spain. The average hourly power in Spain of the electricity consumption due to the electric lights according to the number of household members is shown in Figure 9. The maximum is produced around ten o'clock at night. It can be seen that there are differences between houses with 1 member and houses with 4 members. There are clear differences between weekend and weekdays, with week-on-week consumption generally being higher than weekend (see Table 3).

24

Figure 9 Average power consumption due to the use of electric lights in Spain. The average hour power consumption due to the use of the refrigerator and freezer has been considered constant throughout the day, regardless of the number of household members and whether it is a weekend or not. The average power is 0.090 kW and the average daily consumption is 2.17 kWh. In the same way, the average hourly power of electricity consumption due to standby has been considered constant throughout the day, regardless of the number of household members and whether it is a weekend or not. The average power is 0.026 kW and the average daily consumption is 0.63 kWh. Figure 10 shows the average hourly power of electricity consumption in Spain due to uses not included in the previous categories considered. These uses can be daily personal care appliances (shavers, hair removers, dryers, etc.), kitchen appliances (microwave, toasters, etc.) or house care appliances (drills, lawn mowers, vacuum cleaners, etc.). The highest consumption occurs in the morning (see Table 3).

25

Figure 10 Average power consumption due to the use of others appliances in Spain. Finally, Figure 11 shows the average hourly power in Spain of total electricity consumption. The peak is produced at one-thirty in the afternoon, and about nine in the evening another slightly lower peak occurs. It can be seen that there is an important difference between houses with 1 member and houses with 4 members. Regarding consumption, there is hardly any difference between the weekend and weekdays, although it follows a different pattern. On the weekends, more electricity is consumed the morning; however, in the afternoon more is consumed during weekdays (see Table 3).

Figure 11 Average power consumption in Spain.

26

CONCLUSIONS In this paper, the micro data time use study provided by the Spanish National Institute of Statistics in 2009-2010 was used for developing the power consumption profiles to all housing services both on weekday and weekend, according to the number of members. The data provided by this study permits to know which the active occupation of a household is at each moment and which activity is being developed consequently. With this information it can be estimated, every ten minutes, which appliances are being used and therefore what is the electrical power consumed. The objective of this paper is to know in detail the electricity demand in the residential sector. Such knowledge, generated in the research project and presented in this paper, can constitute a useful tool to energy management, such as for instance being able to propose self-consumption strategies or displacement of DSM curve. Energy management is obviously out of the scope of this paper, but it constitutes the main line of continuity of this piece of research, based on the knowledge generated and presented in this work. Its main utility is in the field of energy efficiency, since the domestic sector has a high impact in the periods of peak demand throughout the day, which can lead to saturation of the network and therefore increase the costs of the electric system in generation and the investment costs for the reinforcement of the electric network. In general, it has been possible to see that two clear peaks occur at one-thirty in the afternoon and another at around nine in the evening. The first peak occurs at the time of day in which a solar photovoltaic installation is in full swing and therefore would be a very good option to reduce this peak. The second peak at nine o'clock at night could be reduced by the use of storage systems or other renewable technologies (wind, cogeneration, etc.). 27

In the case of the washing machine, the peak between 11 a.m. to 12 o'clock in the morning is optimal for self-consumption with photovoltaic energy. This appliance has a totally displaceable load over time and one of the main tools for demand management. The kitchen and the oven have a peak at one-thirty in the afternoon and therefore it is also optimal for photovoltaic energy. The peak of the night could be alleviated by other energies such as wind. In the case of the dishwasher something similar happens with the washing machine and therefore can be moved in time according to convenience. The refrigerator, freezer, and stand-by are relatively constant electrical loads and supply the base load of a home. Little action is possible in these loads, except to minimize the stand-by and acquire refrigerators with high energy efficiency. Finally, the electric lights, TV sets, and computer respond to needs or leisure preferences, so they do not seem to be displaceable over time. These uses, in addition to being more frequent in the afternoon or evening, do not adapt to photovoltaic electrical consumption.

AUTHOR DECLARATION None FUNDING ACKNOWLEDGEMENT This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

REFERENCES 28

1.

López-Rodríguez, M.A., et al., Analysis and modeling of active occupancy of the residential sector in Spain: An indicator of residential electricity consumption. Energy Policy, 2013. 62: p. 742-751.

2.

Torriti, J., Demand Side Management for the European Supergrid: Occupancy variances of European single-person households. Energy Policy, 2012. 44: p. 199-206.

3.

Arghira, N., et al., Prediction of appliances energy use in smart homes. Energy, 2012. 48(1): p. 128-134.

4.

Burlinson, A., M. Giulietti, and G. Battisti, Technology adoption, consumer inattention and heuristic decision-making: Evidence from a UK district heating scheme. Research Policy, 2018. 47(10): p. 1873-1886.

5.

Khalid, A., et al., Towards Dynamic Coordination among Home Appliances Using MultiObjective Energy Optimization for Demand Side Management in Smart Buildings. IEEE Access, 2018. 6: p. 19509-19529.

6.

Aslam, S., et al., An efficient home energy management and power trading in smart grid, in Advances in Intelligent Systems and Computing. 2019. p. 231-241.

7.

De Almeida, A., et al., Characterization of the household electricity consumption in the EU, potential energy savings and specific policy recommendations. Energy and Buildings, 2011. 43(8): p. 1884-1894.

8.

Gautier, A., et al., Self-consumption choice of residential PV owners under netmetering. Energy policy, 2019. 128: p. 648-653.

9.

Swan, L.G. and V.I. Ugursal, Modeling of end-use energy consumption in the residential sector: A review of modeling techniques. Renewable & Sustainable Energy Reviews, 2009. 13(8): p. 1819-1835.

10.

Tso, G.K.F. and K.K.W. Yau, Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks. Energy, 2007. 32(9): p. 17611768.

29

11.

Yamaguchi, Y., et al., A cross analysis of existing methods for modelling household appliance use. Journal of Building Performance Simulation, 2019. 12(2): p. 160-179.

12.

Bottaccioli, L., et al., Realistic Multi-Scale Modeling of Household Electricity Behaviors. Ieee Access, 2019. 7: p. 2467-2489.

13.

Ponocko, J. and J.V. Milanovic, Forecasting Demand Flexibility of Aggregated Residential Load Using Smart Meter Data. Ieee Transactions on Power Systems, 2018. 33(5): p. 5446-5455.

14.

Tureczek, A., P.S. Nielsen, and H. Madsen, Electricity Consumption Clustering Using Smart Meter Data. Energies, 2018. 11(4): p. 18.

15.

Swan, L.G. and V.I. Ugursal, Modeling of end-use energy consumption in the residential sector: A review of modeling techniques. Renewable and Sustainable Energy Reviews, 2009. 13(8): p. 1819-1835.

16.

McKenna, R., et al., A stochastic multi-energy simulation model for UK residential buildings. Energy and Buildings, 2018. 168: p. 470-489.

17.

Khan, I., M.W. Jack, and J. Stephenson, Identifying residential daily electricity-use profiles through time-segmented regression analysis. Energy and Buildings, 2019. 194: p. 232-246.

18.

Panao, M. and M.C. Brito, Modelling aggregate hourly electricity consumption based on bottom-up building stock. Energy and Buildings, 2018. 170: p. 170-182.

19.

Auder, B., et al., Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-Up Forecasting. Energies, 2018. 11(7): p. 22.

20.

Y, K., et al., Predicting intra-day load profiles under time-of-use tariffs using smart meter data. Energy, 2019. 173: p. 959-970.

21.

Beckel, C., et al., Revealing household characteristics from smart meter data. Energy, 2014. 78: p. 397-410.

30

22.

Widén, J., et al., Constructing load profiles for household electricity and hot water from time-use data-Modelling approach and validation. Energy and Buildings, 2009. 41(7): p. 753-768.

23.

Kavgic, M., et al., A review of bottom-up building stock models for energy consumption in the residential sector. Building and Environment, 2010. 45(7): p. 1683-1697.

24.

Wen, L., K. Zhou, and S. Yang, A shape-based clustering method for pattern recognition of residential electricity consumption. Journal of cleaner production, 2019. 212: p. 475488.

25.

Motlagh, O., A. Berry, and L. O'Neil, Clustering of residential electricity customers using load time series. Applied energy, 2019. 237: p. 11-24.

26.

Widén, J. and E. Wäckelgård, A high-resolution stochastic model of domestic activity patterns and electricity demand. Applied Energy, 2010. 87(6): p. 1880-1892.

27.

Richardson, I., et al., Domestic electricity use: A high-resolution energy demand model. Energy and Buildings, 2010. 42(10): p. 1878-1887.

28.

Chiou, Y.S., et al., A high spatial resolution residential energy model based on American Time Use Survey data and the bootstrap sampling method. Energy and Buildings, 2011. 43(12): p. 3528-3538.

29.

Page, J., et al., A generalised stochastic model for the simulation of occupant presence. Energy and Buildings, 2008. 40(2): p. 83-98.

30.

INE, Survey of Employment of Time 2009-2010. 2011, Instituto Nacional de Estadística (Statistics National Institute), Madrid.

31.

Eurostat, E., Guidelines on harmonised European time use surveys. 2004, Office for Official Publications of the European Communities Luxembourg.

32.

IDAE, SECH-SPAHOUSEC Project: Analysis of the Energy Consumption in the Spanish Residential Sector. Final Report, Spanish Institute for Energy Diversification and Saving, Madrid, 2011.

31

33.

España, R.E.d., Proyecto INDEL. Atlas de la demanda eléctrica española. Programa de Investigación y Desarrollo Tecnológico (PIE), REE, 1998.

34.

Firth, S., et al., Identifying trends in the use of domestic appliances from household electricity consumption measurements. Energy and Buildings, 2008. 40(5): p. 926-936.

35.

Gago, E.J., J.O. García, and A.E. Estrella, Development of an energy model for the residential sector: Electricity consumption in Andalusia, Spain. Energy and Buildings, 2011. 43(6): p. 1315-1321.

32