IDEAS home Printed from https://ideas.repec.org/a/eee/recore/v123y2017icp200-207.html
   My bibliography  Save this article

Short-term forecasting of electricity demand for the residential sector using weather and social variables

Author

Listed:
  • Son, Hyojoo
  • Kim, Changwan

Abstract

The aim of this study is to provide a precise model for the one-month-ahead forecast of electricity demand in the residential sector. In this study, a total of 20 influential variables are considered including monthly electricity consumption, 14 weather variables, and five social variables. Based on support vector regression and fuzzy-rough feature selection with particle swarm optimization algorithms, the proposed method established a model with variables that relate to the forecast by ignoring variables that inevitably lead to forecasting errors. The proposed forecasting model was validated using historical data from South Korea between January 1991 and December 2012. The first 240 months were used for training and the remaining 24 for testing. The performance was evaluated using MAPE, MAE, RMSE, MBE, and UPA values. Furthermore, it was compared with that obtained from the artificial neural network, auto-regressive integrated moving average, multiple linear regression models, and the methods proposed in the previous studies. It was found to be superior for every performance measure considered in this study. The proposed method has an advantage over the previous methods because it automatically determines appropriate and necessary variables for a reliable forecast. It is expected that it can contribute to more accurate forecasting of short-term electricity demand in the residential sector. The ability to accurately forecast short-term electricity demand can assist power system operators and market participants in ensuring sustainable electricity planning decisions and securing electricity supply for the consumers.

Suggested Citation

  • Son, Hyojoo & Kim, Changwan, 2017. "Short-term forecasting of electricity demand for the residential sector using weather and social variables," Resources, Conservation & Recycling, Elsevier, vol. 123(C), pages 200-207.
    • Handle: RePEc:eee:recore:v:123:y:2017:i:c:p:200-207
    DOI: 10.1016/j.resconrec.2016.01.016
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0921344916300167
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.resconrec.2016.01.016?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Abdel-Aal, R.E. & Al-Garni, A.Z. & Al-Nassar, Y.N., 1997. "Modelling and forecasting monthly electric energy consumption in eastern Saudi Arabia using abductive networks," Energy, Elsevier, vol. 22(9), pages 911-921.
    2. Yee Yan, Yuk, 1998. "Climate and residential electricity consumption in Hong Kong," Energy, Elsevier, vol. 23(1), pages 17-20.
    3. Badurally Adam, N.R. & Elahee, M.K. & Dauhoo, M.Z., 2011. "Forecasting of peak electricity demand in Mauritius using the non-homogeneous Gompertz diffusion process," Energy, Elsevier, vol. 36(12), pages 6763-6769.
    4. Wang, Jianzhou & Zhu, Wenjin & Zhang, Wenyu & Sun, Donghuai, 2009. "A trend fixed on firstly and seasonal adjustment model combined with the [epsilon]-SVR for short-term forecasting of electricity demand," Energy Policy, Elsevier, vol. 37(11), pages 4901-4909, November.
    5. Sailor, David J. & Muñoz, J.Ricardo, 1997. "Sensitivity of electricity and natural gas consumption to climate in the U.S.A.—Methodology and results for eight states," Energy, Elsevier, vol. 22(10), pages 987-998.
    6. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    7. Dikaios Tserkezos, E., 1992. "Forecasting residential electricity consumption in Greece using monthly and quarterly data," Energy Economics, Elsevier, vol. 14(3), pages 226-232, July.
    8. Altinay, Galip & Karagol, Erdal, 2005. "Electricity consumption and economic growth: Evidence from Turkey," Energy Economics, Elsevier, vol. 27(6), pages 849-856, November.
    9. Lam, Joseph C. & Tang, H.L. & Li, Danny H.W., 2008. "Seasonal variations in residential and commercial sector electricity consumption in Hong Kong," Energy, Elsevier, vol. 33(3), pages 513-523.
    10. Pao, Hsiao-Tien, 2006. "Comparing linear and nonlinear forecasts for Taiwan's electricity consumption," Energy, Elsevier, vol. 31(12), pages 2129-2141.
    11. Pilli-Sihvola, Karoliina & Aatola, Piia & Ollikainen, Markku & Tuomenvirta, Heikki, 2010. "Climate change and electricity consumption--Witnessing increasing or decreasing use and costs?," Energy Policy, Elsevier, vol. 38(5), pages 2409-2419, May.
    12. Pollock, Andrew C. & Macaulay, Alex & Thomson, Mary E. & Onkal, Dilek, 2005. "Performance evaluation of judgemental directional exchange rate predictions," International Journal of Forecasting, Elsevier, vol. 21(3), pages 473-489.
    13. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
    14. Sailor, David J, 2001. "Relating residential and commercial sector electricity loads to climate—evaluating state level sensitivities and vulnerabilities," Energy, Elsevier, vol. 26(7), pages 645-657.
    15. Hong, Wei-Chiang, 2011. "Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm," Energy, Elsevier, vol. 36(9), pages 5568-5578.
    16. Thatcher, Marcus J., 2007. "Modelling changes to electricity demand load duration curves as a consequence of predicted climate change for Australia," Energy, Elsevier, vol. 32(9), pages 1647-1659.
    17. Anin Aroonruengsawat & Maximilian Auffhammer, 2011. "Impacts of Climate Change on Residential Electricity Consumption: Evidence from Billing Data," NBER Chapters, in: The Economics of Climate Change: Adaptations Past and Present, pages 311-342, National Bureau of Economic Research, Inc.
    18. Abdel-Aal, R.E. & Al-Garni, A.Z., 1997. "Forecasting monthly electric energy consumption in eastern Saudi Arabia using univariate time-series analysis," Energy, Elsevier, vol. 22(11), pages 1059-1069.
    19. Mirasgedis, S. & Sarafidis, Y. & Georgopoulou, E. & Lalas, D.P. & Moschovits, M. & Karagiannis, F. & Papakonstantinou, D., 2006. "Models for mid-term electricity demand forecasting incorporating weather influences," Energy, Elsevier, vol. 31(2), pages 208-227.
    20. Abosedra, Salah & Dah, Abdallah & Ghosh, Sajal, 2009. "Electricity consumption and economic growth, the case of Lebanon," Applied Energy, Elsevier, vol. 86(4), pages 429-432, April.
    21. Apadula, Francesco & Bassini, Alessandra & Elli, Alberto & Scapin, Simone, 2012. "Relationships between meteorological variables and monthly electricity demand," Applied Energy, Elsevier, vol. 98(C), pages 346-356.
    22. Ruth, Matthias & Lin, Ai-Chen, 2006. "Regional energy demand and adaptations to climate change: Methodology and application to the state of Maryland, USA," Energy Policy, Elsevier, vol. 34(17), pages 2820-2833, November.
    23. Makridakis, Spyros, 1993. "Accuracy measures: theoretical and practical concerns," International Journal of Forecasting, Elsevier, vol. 9(4), pages 527-529, December.
    24. Lam, Tony N.T. & Wan, Kevin K.W. & Wong, S.L. & Lam, Joseph C., 2010. "Impact of climate change on commercial sector air conditioning energy consumption in subtropical Hong Kong," Applied Energy, Elsevier, vol. 87(7), pages 2321-2327, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mesbaholdin Salami & Farzad Movahedi Sobhani & Mohammad Sadegh Ghazizadeh, 2018. "Short-Term Forecasting of Electricity Supply and Demand by Using the Wavelet-PSO-NNs-SO Technique for Searching in Big Data of Iran’s Electricity Market," Data, MDPI, vol. 3(4), pages 1-26, October.
    2. Lalitpat Aswanuwath & Warut Pannakkong & Jirachai Buddhakulsomsiri & Jessada Karnjana & Van-Nam Huynh, 2023. "An Improved Hybrid Approach for Daily Electricity Peak Demand Forecasting during Disrupted Situations: A Case Study of COVID-19 Impact in Thailand," Energies, MDPI, vol. 17(1), pages 1-31, December.
    3. Jiang, Hongyan & Cheng, Feng & Wu, Cong & Fang, Dianjun & Zeng, Yuhai, 2024. "A multi-period-sequential-index combination method for short-term prediction of small sample data," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    4. Crozier, Constance & Baker, Kyri, 2022. "The effect of renewable electricity generation on the value of cross-border interconnection," Applied Energy, Elsevier, vol. 324(C).
    5. Zhang, Yuejuan & Li, Xian-Xiang & Xin, Rui & Chew, Lup Wai & Liu, Chun-Ho, 2024. "Applicability of data-driven methods in modeling electricity demand-climate nexus: A tale of Singapore and Hong Kong," Energy, Elsevier, vol. 300(C).
    6. Lalitpat Aswanuwath & Warut Pannakkong & Jirachai Buddhakulsomsiri & Jessada Karnjana & Van-Nam Huynh, 2023. "A Hybrid Model of VMD-EMD-FFT, Similar Days Selection Method, Stepwise Regression, and Artificial Neural Network for Daily Electricity Peak Load Forecasting," Energies, MDPI, vol. 16(4), pages 1-24, February.
    7. Kornelia Banasik, 2024. "Influence of Ambient Temperature on the Reliability of Overhead LV Power Lines with Bare Conductors," Energies, MDPI, vol. 17(13), pages 1-10, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Shuai & Yu, Lean & Tang, Ling & Wang, Shouyang, 2011. "A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China," Energy, Elsevier, vol. 36(11), pages 6542-6554.
    2. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    3. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    4. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    5. Psiloglou, B.E. & Giannakopoulos, C. & Majithia, S. & Petrakis, M., 2009. "Factors affecting electricity demand in Athens, Greece and London, UK: A comparative assessment," Energy, Elsevier, vol. 34(11), pages 1855-1863.
    6. Yau, Y.H. & Pean, H.L., 2011. "The climate change impact on air conditioner system and reliability in Malaysia—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(9), pages 4939-4949.
    7. Miller, J. Isaac & Nam, Kyungsik, 2022. "Modeling peak electricity demand: A semiparametric approach using weather-driven cross-temperature response functions," Energy Economics, Elsevier, vol. 114(C).
    8. Tang, Ling & Yu, Lean & Wang, Shuai & Li, Jianping & Wang, Shouyang, 2012. "A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting," Applied Energy, Elsevier, vol. 93(C), pages 432-443.
    9. Nnaemeka Vincent Emodi & Taha Chaiechi & ABM Rabiul Alam Beg, 2018. "The impact of climate change on electricity demand in Australia," Energy & Environment, , vol. 29(7), pages 1263-1297, November.
    10. Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio & Minea, Alina A., 2010. "Analysis and forecasting of nonresidential electricity consumption in Romania," Applied Energy, Elsevier, vol. 87(11), pages 3584-3590, November.
    11. Li, Danny H.W. & Yang, Liu & Lam, Joseph C., 2012. "Impact of climate change on energy use in the built environment in different climate zones – A review," Energy, Elsevier, vol. 42(1), pages 103-112.
    12. Mukherjee, Sayanti & Vineeth, C.R. & Nateghi, Roshanak, 2019. "Evaluating regional climate-electricity demand nexus: A composite Bayesian predictive framework," Applied Energy, Elsevier, vol. 235(C), pages 1561-1582.
    13. Santágata, Daniela M. & Castesana, Paula & Rössler, Cristina E. & Gómez, Darío R., 2017. "Extreme temperature events affecting the electricity distribution system of the metropolitan area of Buenos Aires (1971–2013)," Energy Policy, Elsevier, vol. 106(C), pages 404-414.
    14. Parajuli, Ranjan & Østergaard, Poul Alberg & Dalgaard, Tommy & Pokharel, Govind Raj, 2014. "Energy consumption projection of Nepal: An econometric approach," Renewable Energy, Elsevier, vol. 63(C), pages 432-444.
    15. Vu, D.H. & Muttaqi, K.M. & Agalgaonkar, A.P., 2015. "A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables," Applied Energy, Elsevier, vol. 140(C), pages 385-394.
    16. Chen Zhang & Hua Liao & Zhifu Mi, 2019. "Climate impacts: temperature and electricity consumption," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 99(3), pages 1259-1275, December.
    17. Tung, Ching-Pin & Tseng, Tze-Chi & Huang, An-Lei & Liu, Tzu-Ming & Hu, Ming-Che, 2013. "Impact of climate change on Taiwanese power market determined using linear complementarity model," Applied Energy, Elsevier, vol. 102(C), pages 432-439.
    18. Eshraghi, Hadi & Rodrigo de Queiroz, Anderson & Sankarasubramanian, A. & DeCarolis, Joseph F., 2021. "Quantification of climate-induced interannual variability in residential U.S. electricity demand," Energy, Elsevier, vol. 236(C).
    19. Hirano, Y. & Fujita, T., 2012. "Evaluation of the impact of the urban heat island on residential and commercial energy consumption in Tokyo," Energy, Elsevier, vol. 37(1), pages 371-383.
    20. Attia, Shady & Evrard, Arnaud & Gratia, Elisabeth, 2012. "Development of benchmark models for the Egyptian residential buildings sector," Applied Energy, Elsevier, vol. 94(C), pages 270-284.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:recore:v:123:y:2017:i:c:p:200-207. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Kai Meng (email available below). General contact details of provider: https://www.journals.elsevier.com/resources-conservation-and-recycling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.