Research Article
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A statistical and predictive modeling study to analyze impact of seasons and covid-19 factors on household electricity consumption

Year 2021, Volume: 5 Issue: 4, 252 - 267, 31.12.2021
https://doi.org/10.30521/jes.933674

Abstract

Load is dynamic in nature and changing from aggregated load to disaggregated loads. Hence, need to analyze individual household’s energy consumption pattern. Many factors are contributing to household electricity consumption (HEC). The most influencing factor is the end user’s behavioral aspect. The calendar and seasonal factors are directly affecting user’s behavior activities. This paper consists of two aim, first aim is to validate the performance of traditional predictive models and second aim is to identify the best-fitted predictive model from five predictive models namely: Random Forest, Linear Regression, Support Vector Machine, Neural Network (NN) and Adaptive Boosting. The orange tool is used to simulate the predictive models. The JASP tool is used for statistical analysis of the dataset. From the predictive modeling study, the NN model is the most fitted model. The values of the performance matrix parameter like MSE, RMSE and MAE of the NN model is observed to be 0.558, 0.747 and 0.562 respectively. This study gives insights to researchers and utility companies about traditional predictive models that can predict the HEC under anomaly situations like Covid-19. This study also helps the researchers in using Orange and JASP tool to perform the statistical and predictive modeling.

Thanks

The author would like to thank Symbiosis International (Deemed University) for permitting to carry out the proposed research and to use resources to accomplish the objectives.

References

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  • [2] Teeraratkul, T, O’Neill, D, Lall, S. Shape-Based Approach to Household Electric Load Curve Clustering and Prediction. IEEE Transactions on Smart Grid 2017; 9(5): 5196–206, DOI: 10.1109/TSG.2017.2683461
  • [3] Moezzi, M, Lutzenhiser, L. What’s Missing in Theories of the Residential Energy User. 2010 ACEEE Summer Study on Energy Efficiency in Buildings, Center for Urban Studies Publications and Reports 2010; 207–21. http://archives.pdx.edu/ds/psu/22747
  • [4] Amasyali, K, El-Gohary, NM. A review of data-driven building energy consumption prediction studies. Renewable and Sustainable Energy Reviews 2017; 81: 1192–205. DOI:10.1016/j.rser.2017.04.095
  • [5] Chunekar, A, Sreenivas, A. Towards an understanding of residential electricity consumption in India. Building Research and Information 2018; 47(1): 75–90, DOI: 10.1080/09613218.2018.1489476
  • [6] Li, C, Song, Y, Kaza, N. Urban form and household electricity consumption: A multilevel study. Energy and Buildings 2018; 158: 181–93, DOI: 10.1016/j.enbuild.2017.10.007
  • [7] Lusis, P, Khalilpour, KR, Andrew L, Liebman A. Short-term residential load forecasting: Impact of calendar effects and forecast granularity. Applied Energy 2017; 205: 654–69. DOI:10.1016/j.apenergy.2017.07.114
  • [8] Chitsaz, H, Shaker, H, Zareipour, H, Wood D, Amjady N. Short-term electricity load forecasting of buildings in microgrids. Energy and Buildings 2015; 99: 50–60, DOI: 10.1016/j.enbuild.2015.04.011
  • [9] Hippert, HS, Pedreira, CE, Souza, RC. Neural networks for short-term load forecasting: A review and evaluation. IEEE Transactions on Power Systems 2001; 16(1): 44–55, DOI: 10.1109/59.910780
  • [10] Li, M, Allinson, D, He, M. Seasonal variation in household electricity demand: A comparison of monitored and synthetic daily load profiles. Energy and Buildings 2018; 179:292–300, DOI: 10.1016/j.enbuild.2018.09.018
  • [11] Love, J, Selker, R, Marsman, M, Jamil, T, Dropmann, D, Verhagen, J, Ly, A, Gronau, QF, Šmíra, M, Epskamp, S, Matzke, D, Wild, A, Knight, P, Rouder, JN, Morey, RD, Wagenmakers, E. JASP : Graphical statistical software for common statistical designs. Journal of Statistical Software 2019; 88 (2), DOI: 10.18637/jss. v088.i02
  • [12] Demšar J, Curk, T, Erjavec, A, Gorup, C, Hocevar, T, Milutinovic, M, Mozina, M, Polajnar, M, Toplak, M, Staric, A, Stajdohar, M, Umek, L, Zagar, L, Zbontar, J, Zitnik, M, Zupan, B. Orange: data mining toolbox in python. Journal of Machine Learning Research 2013; 14(1): 2349-2353
  • [13] Breiman, L. Random forests. Machine learning 2001; 45(1): 5-32.
  • [14] Solyali D. A comparative analysis of machine learning approaches for short-/long-term electricity load forecasting in Cyprus. Sustainability (Switzerland) 2020; 12(9), DOI: 10.3390/SU12093612
Year 2021, Volume: 5 Issue: 4, 252 - 267, 31.12.2021
https://doi.org/10.30521/jes.933674

Abstract

References

  • [1] Wen, L, Zhou, K, Yang, S. Load demand forecasting of residential buildings using a deep learning model. Electric Power Systems Research 2020; 179: 106073, DOI: 10.1016/j.epsr.2019.106073
  • [2] Teeraratkul, T, O’Neill, D, Lall, S. Shape-Based Approach to Household Electric Load Curve Clustering and Prediction. IEEE Transactions on Smart Grid 2017; 9(5): 5196–206, DOI: 10.1109/TSG.2017.2683461
  • [3] Moezzi, M, Lutzenhiser, L. What’s Missing in Theories of the Residential Energy User. 2010 ACEEE Summer Study on Energy Efficiency in Buildings, Center for Urban Studies Publications and Reports 2010; 207–21. http://archives.pdx.edu/ds/psu/22747
  • [4] Amasyali, K, El-Gohary, NM. A review of data-driven building energy consumption prediction studies. Renewable and Sustainable Energy Reviews 2017; 81: 1192–205. DOI:10.1016/j.rser.2017.04.095
  • [5] Chunekar, A, Sreenivas, A. Towards an understanding of residential electricity consumption in India. Building Research and Information 2018; 47(1): 75–90, DOI: 10.1080/09613218.2018.1489476
  • [6] Li, C, Song, Y, Kaza, N. Urban form and household electricity consumption: A multilevel study. Energy and Buildings 2018; 158: 181–93, DOI: 10.1016/j.enbuild.2017.10.007
  • [7] Lusis, P, Khalilpour, KR, Andrew L, Liebman A. Short-term residential load forecasting: Impact of calendar effects and forecast granularity. Applied Energy 2017; 205: 654–69. DOI:10.1016/j.apenergy.2017.07.114
  • [8] Chitsaz, H, Shaker, H, Zareipour, H, Wood D, Amjady N. Short-term electricity load forecasting of buildings in microgrids. Energy and Buildings 2015; 99: 50–60, DOI: 10.1016/j.enbuild.2015.04.011
  • [9] Hippert, HS, Pedreira, CE, Souza, RC. Neural networks for short-term load forecasting: A review and evaluation. IEEE Transactions on Power Systems 2001; 16(1): 44–55, DOI: 10.1109/59.910780
  • [10] Li, M, Allinson, D, He, M. Seasonal variation in household electricity demand: A comparison of monitored and synthetic daily load profiles. Energy and Buildings 2018; 179:292–300, DOI: 10.1016/j.enbuild.2018.09.018
  • [11] Love, J, Selker, R, Marsman, M, Jamil, T, Dropmann, D, Verhagen, J, Ly, A, Gronau, QF, Šmíra, M, Epskamp, S, Matzke, D, Wild, A, Knight, P, Rouder, JN, Morey, RD, Wagenmakers, E. JASP : Graphical statistical software for common statistical designs. Journal of Statistical Software 2019; 88 (2), DOI: 10.18637/jss. v088.i02
  • [12] Demšar J, Curk, T, Erjavec, A, Gorup, C, Hocevar, T, Milutinovic, M, Mozina, M, Polajnar, M, Toplak, M, Staric, A, Stajdohar, M, Umek, L, Zagar, L, Zbontar, J, Zitnik, M, Zupan, B. Orange: data mining toolbox in python. Journal of Machine Learning Research 2013; 14(1): 2349-2353
  • [13] Breiman, L. Random forests. Machine learning 2001; 45(1): 5-32.
  • [14] Solyali D. A comparative analysis of machine learning approaches for short-/long-term electricity load forecasting in Cyprus. Sustainability (Switzerland) 2020; 12(9), DOI: 10.3390/SU12093612
There are 14 citations in total.

Details

Primary Language English
Journal Section Research Articles
Authors

Gaikwad Sachin Ramnath 0000-0002-3343-7423

Harikrishnan R 0000-0001-5512-2345

Publication Date December 31, 2021
Acceptance Date November 1, 2021
Published in Issue Year 2021 Volume: 5 Issue: 4

Cite

Vancouver Ramnath GS, R H. A statistical and predictive modeling study to analyze impact of seasons and covid-19 factors on household electricity consumption. Journal of Energy Systems. 2021;5(4):252-67.

Journal of Energy Systems is the official journal of 

European Conference on Renewable Energy Systems (ECRES8756 and


Electrical and Computer Engineering Research Group (ECERG)  8753


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