What is it about?
The unpredictable behavior of the climate affects the power output and causes an unfavorable impact on the stability, reliability and operation of the grid. Thus an accurate forecasting of PV output is a crucial requirement to ensure the stability and reliability of the grid. This paper provides a systematic and critical review on the methods used to forecast PV power output with main focus on the metaheuristic and machine learning methods. Advantages and disadvantages of each method are summarized, based on historical data along with forecasting horizons and input parameters.
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Why is it important?
There is no dertailed review on solar forecasting based on machine learning and metaheuristic techniques before this work.
Perspectives
Writing this article was a great pleasure as it has co-authors with whom I have had long standing collaborations. This article also lead to technical gap for my further research work that hybrid methods produce more forecasting accuracy as compared to individual machine learning and metaheuristic methods.
MUHAMMAD AKHTER
University of Malaya
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This page is a summary of: A review on Forecasting of Photovoltaic Power Generation based on Machine Learning and Metaheuristic Techniques, IET Renewable Power Generation, February 2019, the Institution of Engineering and Technology (the IET),
DOI: 10.1049/iet-rpg.2018.5649.
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