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Load forecasting under changing climatic conditions for the city of Sydney, Australia

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

Abstract

In the current context, climate change has become an unequivocal phenomenon. Although it primarily encompasses change in temperature, nevertheless other weather variables such as rainfall, wind speed, evaporation and humidity can also be affected as a result of climate change. Addressing the impacts of climate change on electricity demand is essential for predicting the future demand. For example, cooling and heating requirements change significantly with respect to climate change that may result to the change in electricity load demand. In this paper, a backward elimination based multiple regression approach is proposed for analyzing the influence of climatic variables on load forecasting. A correlation analysis has been carried out using Pearson’s correlation coefficient to examine the interdependency between different climatic variables in the context of Sydney, one of the most densely populated cities in Australia. Regression based analysis has been performed to examine the relationship between per capita electricity demand and associated climatic variables. ‘Degree Days’ concept has been utilized to determine balance point temperature. Backward elimination based multiple regression is used to exclude non-significant climatic variables and evaluate the sensitivity of significant variables related to the load demand. Average change in future per capita electricity demand has been predicted using the proposed approach for the city of Sydney, Australia. Results indicate that the demand for Sydney will increase by 6% by 2030.

Suggested Citation

  • Ahmed, T. & Vu, D.H. & Muttaqi, K.M. & Agalgaonkar, A.P., 2018. "Load forecasting under changing climatic conditions for the city of Sydney, Australia," Energy, Elsevier, vol. 142(C), pages 911-919.
    • Handle: RePEc:eee:energy:v:142:y:2018:i:c:p:911-919
    DOI: 10.1016/j.energy.2017.10.070
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    Cited by:

    1. Barman, Mayur & Dev Choudhury, Nalin Behari, 2019. "Season specific approach for short-term load forecasting based on hybrid FA-SVM and similarity concept," Energy, Elsevier, vol. 174(C), pages 886-896.
    2. Marwen Elkamel & Lily Schleider & Eduardo L. Pasiliao & Ali Diabat & Qipeng P. Zheng, 2020. "Long-Term Electricity Demand Prediction via Socioeconomic Factors—A Machine Learning Approach with Florida as a Case Study," Energies, MDPI, vol. 13(15), pages 1-21, August.
    3. Zhang, Ning & Li, Zhiying & Zou, Xun & Quiring, Steven M., 2019. "Comparison of three short-term load forecast models in Southern California," Energy, Elsevier, vol. 189(C).
    4. Zhu, Jizhong & Dong, Hanjiang & Zheng, Weiye & Li, Shenglin & Huang, Yanting & Xi, Lei, 2022. "Review and prospect of data-driven techniques for load forecasting in integrated energy systems," Applied Energy, Elsevier, vol. 321(C).
    5. Zhang, Guoqiang & Guo, Jifeng, 2020. "A novel ensemble method for hourly residential electricity consumption forecasting by imaging time series," Energy, Elsevier, vol. 203(C).
    6. Alberini, Anna & Prettico, Giuseppe & Shen, Chang & Torriti, Jacopo, 2019. "Hot weather and residential hourly electricity demand in Italy," Energy, Elsevier, vol. 177(C), pages 44-56.
    7. Rafati, Amir & Joorabian, Mahmood & Mashhour, Elaheh, 2020. "An efficient hour-ahead electrical load forecasting method based on innovative features," Energy, Elsevier, vol. 201(C).

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