期刊名称：International Journal of Energy Economics and Policy
电子版ISSN：2146-4553
出版年度：2020
卷号：10
期号：5
页码：208-219
DOI：10.32479/ijeep.9533
出版社：EconJournals
摘要：The use of intermittent power supplies, such as solar energy, has posed a complex conundrum when it comes to the prediction of the next days' supply. There have been several approaches developed to predict the power production using Machine Learning methods, such as Artificial Neural Networks (ANNs). In this work, we propose the use of weather variables, such as ambient temperature, solar irradiation, and wind speed, collected from a weather station of a Photovoltaic (PV) system located in Amman, Jordan. The objective is to substitute the aforementioned ambient temperature with the more realistic PV cell temperature with a desire of achieving better prediction results. To this aim, ten physics-based models have been investigated to determine the cell temperature, and those models have been validated using measured PV cell temperatures by computing the Root Mean Square Error ( RMSE ). Then, the model with the lowest RMSE has been adopted in training a data-driven prediction model. The proposed prediction model is to use an ANN compared to the well-known benchmark model from the literature, i.e., Multiple Linear Regression (MLR). The results obtained, using standard performance metrics, have displayed the importance of considering the cell temperature when predicting the PV power output.
关键词：Renewable Energy;Photovoltaic;Prediction;Cell temperature;Multiple Linear Regression;Artificial Neural Networks.
