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Machine Learning Techniques for Renewable Energy Forecasting: A Comprehensive Review

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Computational Intelligence Techniques for Green Smart Cities

Part of the book series: Green Energy and Technology ((GREEN))

Abstract

Over the past decade, renewable energy resources, such as wind, solar, biomass, ocean energy and other kinds of energy, are becoming attractive technologies for building green smart cities. These new forms of energy can complete the world’s energy demand, protect the environment and provide energy security. Statistics have shown that renewable energy resources offer between 15 and 30% of the world’s energy. Moreover, the production and consumption of different kinds of renewable energy are constantly increasing every year. However, forecasting renewable resources in terms of production and consumption is becoming more vital for the decision-making process in the energy sector. Indeed, the accurate forecasting of renewable energy permits to ensure optimal management of energy. In this context, machine learning techniques represent a promising solution to deal with forecasting issues. Several solutions and forecasting models based on machine learning have been extensively proposed in the literature for predicting power energy that should be deployed for future smart cities. This chapter aims to conduct a systematic mapping study to analyze and synthesize studies concerning machine learning techniques for forecasting renewable resources. Therefore, a total number of 86 relevant papers published on this subject between January 1, 2007, and December 31, 2021, were carefully selected. The selected articles were classified and analyzed according to the following criteria: channel and year of publication, research type, study domain, study context, study category and machine learning techniques used for forecasting renewable resources. The results showed that wind energy and solar energy were used massively in selected papers, and the forecasting of power production based on hourly forecast model and minutely forecast was the primary interest in the majority of selected papers. Furthermore, artificial neural network (ANN) and deep neural network (DNN) were the most regression algorithms used to predict renewable energy sources.

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Authors and Affiliations

  1. National Superior School of Mines, Rabat, Morocco

    Rajae Gaamouche & Abdennebi El Hasnaoui

  2. ENEA—C.R. Casaccia Via Anguillarese, Rome, Italy

    Marta Chinnici

  3. University Hassan II, Higher Normal School of Casablanca, Casablanca, Morocco

    Mohamed Lahby & Youness Abakarim

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  1. Rajae Gaamouche
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  2. Marta Chinnici
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  3. Mohamed Lahby
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  4. Youness Abakarim
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  5. Abdennebi El Hasnaoui
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Correspondence to Mohamed Lahby .

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  1. ENS, Hassan II University, Casablanca, Morocco

    Mohamed Lahby

  2. Hamad bin Khalifa University, Doha, Qatar

    Ala Al-Fuqaha

  3. Sultan Moulay Slimane University, Beni-Mellal, Morocco

    Yassine Maleh

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Gaamouche, R., Chinnici, M., Lahby, M., Abakarim, Y., Hasnaoui, A. (2022). Machine Learning Techniques for Renewable Energy Forecasting: A Comprehensive Review. In: Lahby, M., Al-Fuqaha, A., Maleh, Y. (eds) Computational Intelligence Techniques for Green Smart Cities. Green Energy and Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-96429-0_1

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