Widen (2009) markov chain modelling of high-resolution activity patterns and household electricity demand
MARKOV-CHAIN MODELLING OF HIGH-RESOLUTION ACTIVITY PATTERNS AND HOUSEHOLD ELECTRICITY DEMAND Joakim Widén1*, Kajsa Ellegård2, Ewa Wäckelgård1 1. Department of Engineering Sciences, Uppsala University P.O. Box 534, SE-751 21 Uppsala, Sweden 2. Department of Technology and Social Change, Linköping University SE-581 83 Linköping, Sweden * Email address: email@example.comABSTRACT In this paper we present briefly a model framework for generation of high-resolved and end-use-specific household electricity load profiles with a probabilistic approach. A non-homogeneous Markov-chain model is used for generation of synthetic time-use data series, fromwhich electricity demand is calculated with a bottom-up approach. Household activities areconnected to a set of appliances and the load profiles of these appliances are added together basedon the distribution of activities in the time-use data. A detailed set of time use in Swedishhouseholds is used for estimation of transition probabilities in the Markov-chain model. Loadprofiles of individual appliances are determined from measurements on appliance level in anumber of households in a recent large-scale survey by the Swedish Energy Agency (SEA). It isshown that the model realistically reproduces both load profiles for individual households andcoincident load of a large number of households.Keywords: Household electricity; Markov chain; Bottom-up; Time-use data1. INTRODUCTION Detailed simulations of distributed generation have recently prompted the need for improvedmodels of household electricity demand (Thomson & Infield, 2007). Probabilistic methods havebeen used in studies of photovoltaic (PV) integration in Sweden, e.g. in (Viawan, 2008) whereMonte Carlo simulations were performed, based on samplings of measured PV production andaggregate electricity demand. However, for more realistic simulations, it is important toreproduce both randomness and regularities in production and demand. For example, both PVoutput and load are highly time-dependent and at the same time subject to random variation andcoincident behaviour. Load modelling is a complex task and, consequently, load models often exhibit a high degree ofcomplexity, see for example Stokes (2005) and Capasso et al. (1994). In particular, the variationand regularity caused by peoples’ habits and household activities are hard to capture in amathematical model. As shown in previous studies, time-use data – detailed sequences of dailyactivities in households, usually collected through diaries – can improve modelling of thebehavioural component of the household load (Kall & Widén, 2007; Widén et al., 2009a).Richardson et al. (2008) modelled domestic occupancy patterns from time-use data with aMarkov-chain model. The same approach can be applied to all electricity-demanding householdactivities and combined with a conversion model to generate electricity load patterns.1.1 Aim In this paper we present briefly a model that generates high-resolved household electricity loadprofiles with a combined Markov-chain and bottom-up approach. All important electricity-dependent activities are described by a multi-state non-homogeneous Markov chain and areconnected to appliance loads in order to generate end-use-specific load profiles. A detailed set of
time-use data is used, for which a classification scheme makes it possible to identify a wide rangeof activities. Load data for individual appliances have been obtained from recent measurementsby the Swedish Energy Agency (SEA). The model outlined in this paper is described andvalidated in more detail in a coming journal paper (Widén et al., 2009b).1.2 Structure of the paper The utilized data are presented in section 2. Section 3 describes the model framework and howthe data are used. Features of synthetic activity and load patterns generated with the model areshown, in comparison to corresponding measured data, in section 4. Section 5 summarizes andbriefly discusses the main findings.2. DATA2.1 Time-use data The utilized time-use data set was collected by Statistics Sweden in a pilot study of time usefrom 1996, covering 431 persons in 169 households. Each person in the participating householdswrote a time diary on one weekday and one weekend day, reporting the sequences of activitiesperformed, together with various additional types of data. The time resolution is mainly 5minutes, although some households have reported on a 1-minute basis. In the data set, theactivities have been coded with activity codes referring to a categorization scheme thatencompasses several levels of abstraction (Ellegård & Cooper, 2004). The sample of households in the data covers individuals aged between 10 and 97 years, invarious family constellations and geographical settings. An extensive background material givesinsight into the socio-economical status of the households, for example the type of dwelling(apartments and detached houses).2.2 Solar irradiation data Data for solar irradiance are used for generation of lighting demand, through conversion ofhorizontal direct and diffuse irradiance to vertical global illuminance, and application of a linearmodel for indoor lighting. Synthetic irradiation data were generated for Stockholm, Sweden, withthe climate database and simulation tool Meteonorm 6.0 (Meteotest, 2009) on a one-minuteresolution.2.3 Power demand of appliances Data on the power demand of individual appliances were obtained from the recentmeasurement study of the SEA. In a behavioural study connected to the survey, end-use-specificmeasurements in 14 households were analyzed and gone through for consistency (Karlsson &Widén, 2008). Representative duty cycles for washing and dishwashing machines weredetermined from these data, as well as cold appliance load cycles and standby consumption ofcomputer, tv etc. Active-use power demand of many appliances was taken from the previouslypresented activity-to-electricity conversion model (Widén et al., 2009a).3. MODEL FRAMEWORK3.1 Generation of synthetic activity patterns In the time-use data the household members’ days are represented as series of activities. Eachindividual can be seen as making a trajectory between a number of different states, correspondingto the activities. The patterns thus produced exhibit randomness, although there are obviousregularities in them. These features can be reproduced by a non-homogeneous Markov-chainmodel (Cinlar, 1975).
In such a model, we assume that there are N states, one of which must be occupied in everytime step. In every time step the individual changes to another state or stays in the current state,with certain transition probabilities pjk, where j is the current state and k is the state at the nexttime step. Obviously, pjj is the probability of staying in the current state. In a non-homogeneousMarkov chain, these probabilities are time-dependent. For example, the probability of changingfrom the state ‘sleeping’ to ‘being awake’ supposedly increases over the course of the morning. The transition probabilities are easily determined from existing time-use data. In each time step,the transitions from the current state to every other state are counted and divided by the totalnumber of changes from that state (including remaining in the current state). For a relativelysmall data set, as the one used in this study, the number of persons on which to calculate thetransition probabilities is so small that transition probabilities cannot be determined for all timesteps. Therefore, hourly means are evaluated, and the transition probabilities are assumedconstant in every hourly interval. Also, because of differences found in Widén et al. (2009a),detached houses and apartments are treated separately. With a larger material, separate transitionprobability sets could have been determined for even more subsets of the time-use data, forexample households of the same sizes, as in Richardson et al. (2008). To keep the number of states at a minimum, activities in the time-use data set were categorizedinto ten main states connected to use of household electricity: ‘away’, ‘sleeping’, ‘cooking’,‘dishwashing’, ‘washing’, ‘drying’, ‘tv/vcr/dvd’, ‘computer’, ‘audio’ and ‘other’. Synthetic dataseries were generated from the set of transition probabilities by setting an initial state for eachsimulated individual and evaluating state changes in every time step.3.2 Activity-to-power conversion schemes The model for conversion of the generated activity patterns to electricity load data is arefinement of a previously developed model (Widén et al., 2009a). Each activity is connected to acertain appliance with a predefined load. As discussed in Widén et al. (2009a), appliance use inthese basic conversion schemes occurs either during or after the activity. The former schemeholds for computer, tv, etc., where the load is constant during use. When not actively used, astand-by power is assumed. The second scheme holds for washing, dishwashing and drying,where power is demanded during a duty cycle that begins after the active use of the appliance isfinished. As in Widén et al. (2009a), two special cases have to be treated: lighting, which is modelledbased on when persons are at home and awake, and cold appliances, which are unrelated to theactivities. Active individuals, present in the home, are assumed to demand a lighting powerdependent on the daylight level, calculated from the irradiance data. This model is based on thepreviously developed lighting model in Widén et al. (2009a) and is described in more detail in acoming journal paper (Widén et al., 2009c). Stochasticity is introduced in the modelled coldappliances data through random generation of duty cycle energy use and intermediate time spans,with distributions determined from measured SEA appliance data. For simplicity, all households are assumed to use exactly the same appliance set. In a moreadvanced simulation, different sets of appliances with different energy consumption could beassumed, however with a much more extensive demand of input data.4. PERFORMANCE OF THE MODEL4.1 Activity patterns The most important abilities of the activity-generating part of the model is the introduction ofrealistic stochasticity in the synthetic activity patterns, and the reproduction of the coincidentbehaviour of the load. Per definition, the aggregate asymptotic frequency curve of activities,when the number of persons approaches infinity, equals that of the data used for estimation oftransition probabilities (Cinlar, 1975).
As Figure 1 suggests, the randomness as well as the regularity of the activity frequency curvesare reproduced by the model. The figure shows the frequency curves of the occupation of allstates except the ‘away’ state, that is, all persons being at home, awake as well as sleeping. It alsoshows coincidence of activity patterns, resulting in a smoothing-out of the frequency curve for thelarger number of households, both in the original time-use data and the reproduction of themodel. This suggests realistic coincidence of the appliance loads thus distributed by the time-usedata. Persons 4 0 Modelled 4 Persons 0 0 24 48 72 96 120 Time (hours) Measured (a) 4 persons 80 Persons 0 Modelled 80 Persons 0 0 24 48 72 96 120 Time (hours) Measured (b) 80 personsFigure 1. Modelled and real home occupancy patterns for 4 and 80 persons, showing coincidence of activities, weekday. The measured data is the recorded time use of 4 and 80 persons,respectively, randomly sampled into five subsets and ordered in series representing five different ‘days’.4.2 Electricity patterns In the following, the model output is compared to measured data for one, 13 and hundreds ofhouseholds, in order to determine how realistic the generated load patterns are. For brevity, onlyresults for detached houses are presented. The load data generated by the model must be realistic both for a small number of householdsas well as for a large number of households. Figure 2 compares a modelled load curve for anindividual household to a measured curve, with the end uses shown specifically. Note that thecurves are totally unrelated, so they should not be identical. However, they should exhibit similarfeatures. Indeed, the modelled and real data are highly similar. The irregular pattern of peaksfrom washing, dishwashing and cooking is present in both of the figures, as well as the recurringload pattern of the cold appliances and the more regular pattern of increasing power demand fromactively used appliances and lighting in the evening.
5 Cold appliances Lighting 4 Cooking Dishwashing 3 Washing kW TV, DVD, VCR 2 Computer Stereo 1 00:00 12:00 24:00 12:00 24:00 Time of day (a) Example of modeled data 5 Cold appliances Lighting 4 Cooking Dishwashing 3 Washing kW TV, DVD, VCR 2 Computer Stereo 1 00:00 12:00 24:00 12:00 24:00 Time of day (b) Example of measured data Figure 2. Examples of 10-minute end-use-specific load curves of individual households during two successive days. The modeled data are for a simulated 4-member household in a detached house. The measured data are for a real 4-member detached-house household, measured during October 2006. In aggregate load curves, the random coincidence of the different loads evens out the heavypower fluctuations seen in the individual households’ demand. Figure 3, comparing the aggregateload of 13 modelled household loads to the aggregate load of the same number of measuredhousehold loads, shows that the model reproduces this smoothing of the demand. The load issomewhat lower in (b), which could be that it is to nearly one half based on apartment loads,which in general are lower than the load of detached houses. Differences between weekdays andweekend days (a more even load curve on weekends) are seen in both the modelled and themeasured data. Note also that the randomness and the peaks in both figures are similar. Figure 4, finally, shows the correspondence between the mean hourly load curve generated bythe model for a large number of households to the mean load curve based on all finishedhousehold measurements in the SEA’s survey in 2007. The similarity is evident for most enduses. The differences are basically the same as those discussed in (Widén et al., 2009a), mostnotably for cooking, which, in lack of detailed information about appliance use, is assumed to usea constant power during the activity. The computer category was also problematic in Widén et al.(2009a), because of the much lower computer use in 1996. However, in this refinement of themodel, activities that were assumed to have been ‘computerized’ during the last decade wereincluded, and so the resulting load curve is closer to the recent measurements. It should be noted,also, that the transition probabilities are based on fewer data points than the SEA data, andtherefore the aggregate modelled load can appear more variable.
20 15 kW 10 5 0 0 24 48 72 96 120 144 168 Time (hours) (a) Example of modeled data 20 15 kW 10 5 0 0 24 48 72 96 120 144 168 Time (hours) (b) Example of measured data Figure 3. Examples of aggregate load curves for 13 households during a week (five weekdays and two weekend days). The modeled load is for 13 simulated detached houses. The measured load is for six apartments and seven detached houses, measured in the summer and autumn of 2006. 700 700 Cold appliances Cold appliances Lighting Lighting 600 Cooking 600 Cooking Dishwashing Dishwashing Washing Washing TV, DVD, VCR TV, DVD, VCR 500 500 Computer Computer Stereo Stereo 400 400 Wh/h Wh/h 300 300 200 200 100 100 0 0 1 6 12 18 24 1 6 12 18 24 Hour of day Hour of day (a) Modelled (b) Measured Figure 4. Mean hourly load curves, detached houses, weekday. The modelled data in (a) arebased on 200 simulated households on seven summer days and seven winter days. Measured data from the SEA’s measurement survey. A close-to-constant additional load category is not shown in (b).
5. CONCLUDING REMARKS It has been shown that the model generates household electricity load profiles that are realisticin all the important aspects covered here: coincidence, random variation, regularity and relativedistribution on end uses. Some points for possible improvement of the model can be identified: Firstly, the appliance setcould be varied or distributed differently among households to reflect real appliance ownership.Secondly, a larger time-use data set would allow separate sets of transition probabilities to beestimated for different household types, which would make it possible to include more systematicdifferences between household types. Both of these options however require more input data and increase the model complexity.There is obviously a balance between low complexity and high output detail. With the currentdata, the model appears to perform well despite the lack of more complex assumptions.ACKNOWLEDGEMENTS The work has been carried out under the auspices of The Energy Systems Programme, which isprimarily financed by the Swedish Energy Agency. Part of the work with refinement of thebottom-up model for electricity demand was carried out in a project financed by Göteborg Energi.The authors also wish to thank Annica Nilsson, Department of Engineering Sciences, UppsalaUniversity, for the irradiation-to-daylight data conversion.REFERENCESCapasso, A., Grattieri, W., Lamedica, R., Prudenzi, A., 1994. A bottom-up approach to residential load modeling, IEEE Transactions on Power Systems 9, 957–964.Cinlar, E., 1975. Introduction to stochastic processes. Prentice-Hall.Ellegård, K., Cooper, M., 2004. Complexity in daily life - a 3D-visualization showing activity patterns in their context, electronic International Journal of Time Use Research 1 (1), 37-59.Kall, A.S., Widén, J., 2007. Det stora i det lilla och det lilla i det stora - teknik och politik i omställningen av energisystemet, Working Paper 37, Energy Systems Programme, Linköping University, Sweden.Karlsson, K., Widén, J., 2008. Hushållens elanvändningsmönster identifierade i vardagens aktiviteter, Working Paper, Department of Technology and Social Change, Linköping University, Sweden.Meteotest, 2009. http://www.meteotest.ch.Richardson, I., Thomson, M., Infield, D., 2008. A high-resolution domestic building occupancy model for energy demand simulations. Energy and Buildings 40 (8), 1560-1566.Stokes, M., 2005. Removing barriers to embedded generation: a fine-grained load model to support low voltage network performance analysis. PhD thesis, Institute of Energy and Sustainable Development, DeMontfort University, UK.Thomson, M., Infield, D.G., 2007. Impact of widespread photovoltaics generation on distribution systems. IET Renewable Power Generation 1, 33-40.Viawan, F., 2008. Voltage control and voltage stability of power distribution systems in the presence of distributed generation. PhD thesis, Department of Energy and Environment, Chalmers University of Technology, Sweden.Widén, J., Lundh, M., Vassileva, I., Wäckelgård, E., Ellegård, K., 2009a. Constructing load profiles for household electricity and hot water from time-use data - modelling approach and validation, resubmitted to Energy and Buildings after minor revisions.Widén, J., Wäckelgård, E., 2009b. A high-resolved stochastic model of household electricity demand, in manuscript.Widén, J., Nilsson, A., Wäckelgård, E., 2009c. A combined Markov-chain and bottom-up approach to domestic lighting modelling, in manuscript.