Abstract
Over the past few years, solar power has significantly increased in popularity as a renewable energy. In the context of electricity generation, solar power offers clean and accessible energy, as it is not associated with global warming and pollution. The main challenge of solar power is its uncontrollable fluctuation since it is highly depending on other weather variables. Thus, forecasting energy generation is important for smart grid operators and solar electricity providers since they are required to ensure the power continuity in order to dispatch and properly prepare to store the energy. In this study, we propose an efficient comparison framework for forecasting the solar power that will be generated 36 h in advance from Yeongam solar power plant located in South Jeolla Province, South Korea. The results show a comparative analysis of the state-of-the-art techniques for solar power generation.
Highlights
root mean square error (RMSE) penalizes the higher difference between prediction and actual solar power, and mean absolute error (MAE) directly takes the average of offsets
To measure the subsets of the resulting models and to analyze the most important variables that improve the performance of the models, four approaches where compared: Mallows Cp (Cp ), Akaike’s information criteria (AIC), Bayesian information criteria (BIC) and adjusted R2, represented as follows: Cp = 1/N (MSE × N + 2dσ2 ), (5)
Proposeda acomparative comparative of learning techniques for the wewe proposed of different differentmachine machine learning techniques for the prediction of solar power generation 36 h ahead
Summary
Renewable energy is considered in different countries as source of electricity production. The principal characteristic of renewable energy is highly dependent on weather factors, making it difficult for obtaining a stable energy production. This characteristic leads to a production level that fluctuates with weather conditions [1]. Power companies must guarantee a precise balance between the production and consumption of electricity. They need to maintain a stability of services to their customers, forestalling unanticipated disturbances in the energy production
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