Electricity load forecasting: a systematic review
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(2020) 7:13
Journal of Electrical Systems and Information Technology Open Access
REVIEW
Electricity load forecasting: a systematic review Isaac Kofi Nti1* , Moses Teimeh2, Owusu Nyarko‑Boateng3 and Adebayo Felix Adekoya3
*Correspondence: [email protected] 1 Department of Computer Science, Sunyani Technical University, Box 206, Sunyani, Ghana Full list of author information is available at the end of the article
Abstract The economic growth of every nation is highly related to its electricity infrastructure, network, and availability since electricity has become the central part of everyday life in this modern world. Hence, the global demand for electricity for residential and com‑ mercial purposes has seen an incredible increase. On the other side, electricity prices keep fluctuating over the past years and not mentioning the inadequacy in electricity generation to meet global demand. As a solution to this, numerous studies aimed at estimating future electrical energy demand for residential and commercial purposes to enable electricity generators, distributors, and suppliers to plan effectively ahead and promote energy conservation among the users. Notwithstanding, load forecasting is one of the major problems facing the power industry since the inception of electric power. The current study tried to undertake a systematic and critical review of about seventy-seven (77) relevant previous works reported in academic journals over nine years (2010–2020) in electricity demand forecasting. Specifically, attention was given to the following themes: (i) The forecasting algorithms used and their fitting ability in this field, (ii) the theories and factors affecting electricity consumption and the origin of research work, (iii) the relevant accuracy and error metrics applied in electricity load forecasting, and (iv) the forecasting period. The results revealed that 90% out of the top nine models used in electricity forecasting was artificial intelligence based, with artificial neural network (ANN) representing 28%. In this scope, ANN models were pri‑ marily used for short-term electricity forecasting where electrical energy consumption patterns are complicated. Concerning the accuracy metrics used, it was observed that root-mean-square error (RMSE) (38%) was the most used error metric among electricity forecasters, followed by mean absolute percentage error MAPE (35%). The study further revealed that 50% of electricity demand forecasting was based on weather and eco‑ nomic parameters, 8.33% on household lifestyle, 38.33% on historical energy consump‑ tion, and 3.33% on stock indices. Finally, we recap the challenges and opportunities for further research in electricity load forecasting locally and globally. Keywords: Electricity load forecasting, Machine learning, Electrical energy demand, Artificial intelligence
Background Electricity is the pivot in upholding highly technologically advanced industrialisation in every economy [1–3]. Almost every activity done in this modern era hinges on electricity. The demand an
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