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• W4309303702 abstract "Changes in the quantity of solar energy reaching the Earth's surface have a straight influence on the climate as air temperatures have their origin in the absorption of radiant energy from the Sun. Solar radiation is the largest and purest source of renewable energy on earth with an extremely low carbon footprint required for power generation. Knowing expected solar radiation beforehand has a great utility not only from the economic perspective but also from the social perspective, especially for the countries like UAE which are part of the Sun Belt. The main objective of this paper is to explore three different univariate time series modeling techniques, namely: seasonal auto-regressive integrated moving average (SARIMA), K-nearest neighbors (KNN) and recursive neural network-long short term memory (RNN-LSTM) to predict the monthly average solar radiation per day in UAE. The best model was selected based on the root mean square error on training as well as testing datasets. The results of this study show that for solar radiation forecasting the machine learning techniques RNN-LSTM and KNN significantly outperform the classical time series modeling technique of SARIMA. While RNN-LSTM and KNN perform almost equally well, RNN-LSTM has a slight edge over KNN for both short-term and long-term forecasting. The findings of this paper throw light on how different univariate time series forecasting models perform in predicting solar radiation, which depends on multiple tangible and intangible atmospheric parameters which are difficult to capture or not readily available historically. Easy, quick, and accurate methods for short-term and long-term solar radiation prediction would help UAE (and other countries in Sun-Belt) to effectively manage the sporadic nature of solar radiation due to climate changes which sometimes compromise the functioning of electricity grids and in turn the electricity supply of photovoltaic plants." @default.
• W4309303702 created "2022-11-25" @default.
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• W4309303702 date "2023-01-01" @default.
• W4309303702 modified "2023-10-16" @default.
• W4309303702 title "Climate change: Prediction of solar radiation using advanced machine learning techniques" @default.
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• W4309303702 doi "https://doi.org/10.1016/b978-0-323-99714-0.00017-0" @default.
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