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Short-term electricity demand forecasting using autoregressive based time varying model incorporating representative data adjustment

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  • Vu, D.H.
  • Muttaqi, K.M.
  • Agalgaonkar, A.P.
  • Bouzerdoum, A.

Abstract

This paper presents the development of an autoregressive based time varying (ARTV) model to forecast electricity demand in a short-term period. The ARTV model is developed based on an autoregressive model by allowing its coefficients to be updated at pre-set time intervals. The updated coefficients help to enhance the relationships between electricity demand and its own historical values, and accordingly improve the performance of the model. In addition, a representative data adjustment procedure including a similar-day-replacement technique and a data-shifting algorithm is proposed in this paper to cultivate the historical demand data. These techniques help cleanse the raw data by mitigating the abnormal data points when daylight saving and holiday occur. Consequently, the robustness of the model is significantly enhanced, and accordingly the overall forecasting accuracy of the model is considerably improved. A case study has been reported in this paper by acquiring the relevant data for the state of New South Wales, Australia. The results show that the proposed model outperforms conventional seasonal autoregressive and neural network models in short term electricity demand forecasting.

Suggested Citation

  • Vu, D.H. & Muttaqi, K.M. & Agalgaonkar, A.P. & Bouzerdoum, A., 2017. "Short-term electricity demand forecasting using autoregressive based time varying model incorporating representative data adjustment," Applied Energy, Elsevier, vol. 205(C), pages 790-801.
    • Handle: RePEc:eee:appene:v:205:y:2017:i:c:p:790-801
    DOI: 10.1016/j.apenergy.2017.08.135
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    1. Taylor, James W., 2010. "Triple seasonal methods for short-term electricity demand forecasting," European Journal of Operational Research, Elsevier, vol. 204(1), pages 139-152, July.
    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. De Felice, Matteo & Alessandri, Andrea & Catalano, Franco, 2015. "Seasonal climate forecasts for medium-term electricity demand forecasting," Applied Energy, Elsevier, vol. 137(C), pages 435-444.
    4. Xiao, Liye & Shao, Wei & Wang, Chen & Zhang, Kequan & Lu, Haiyan, 2016. "Research and application of a hybrid model based on multi-objective optimization for electrical load forecasting," Applied Energy, Elsevier, vol. 180(C), pages 213-233.
    5. Takeda, Hisashi & Tamura, Yoshiyasu & Sato, Seisho, 2016. "Using the ensemble Kalman filter for electricity load forecasting and analysis," Energy, Elsevier, vol. 104(C), pages 184-198.
    6. Zhou, Kaile & Yang, Shanlin & Shao, Zhen, 2016. "Energy Internet: The business perspective," Applied Energy, Elsevier, vol. 178(C), pages 212-222.
    7. Yukseltan, Ergun & Yucekaya, Ahmet & Bilge, Ayse Humeyra, 2017. "Forecasting electricity demand for Turkey: Modeling periodic variations and demand segregation," Applied Energy, Elsevier, vol. 193(C), pages 287-296.
    8. Fang, Tingting & Lahdelma, Risto, 2016. "Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system," Applied Energy, Elsevier, vol. 179(C), pages 544-552.
    9. Feng, Yonghan & Ryan, Sarah M., 2016. "Day-ahead hourly electricity load modeling by functional regression," Applied Energy, Elsevier, vol. 170(C), pages 455-465.
    10. Pappas, S.Sp. & Ekonomou, L. & Karamousantas, D.Ch. & Chatzarakis, G.E. & Katsikas, S.K. & Liatsis, P., 2008. "Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models," Energy, Elsevier, vol. 33(9), pages 1353-1360.
    11. Xiao, Liye & Shao, Wei & Liang, Tulu & Wang, Chen, 2016. "A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting," Applied Energy, Elsevier, vol. 167(C), pages 135-153.
    12. Saab, Samer & Badr, Elie & Nasr, George, 2001. "Univariate modeling and forecasting of energy consumption: the case of electricity in Lebanon," Energy, Elsevier, vol. 26(1), pages 1-14.
    13. Che, JinXing & Wang, JianZhou, 2014. "Short-term load forecasting using a kernel-based support vector regression combination model," Applied Energy, Elsevier, vol. 132(C), pages 602-609.
    Full references (including those not matched with items on IDEAS)

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    9. Jonathan Berrisch & Micha{l} Narajewski & Florian Ziel, 2022. "High-Resolution Peak Demand Estimation Using Generalized Additive Models and Deep Neural Networks," Papers 2203.03342, arXiv.org, revised Nov 2022.
    10. Pesantez, Jorge E. & Li, Binbin & Lee, Christopher & Zhao, Zhizhen & Butala, Mark & Stillwell, Ashlynn S., 2023. "A Comparison Study of Predictive Models for Electricity Demand in a Diverse Urban Environment," Energy, Elsevier, vol. 283(C).
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