Short-Term Solar Power Forecasting Using Genetic Algorithms: An Application Using South African Data

• Main Authors: Mamphaga Ratshilengo, Caston Sigauke, Alphonce Bere
• Format: Article
• Language: English
• Published: MDPI AG 2021-05-01
• Series: Applied Sciences
• Subjects: Giacommini–White test; global horizontal irradiance; genetic algorithm; Lasso; machine learning; Murphy diagram
• Online Access: https://www.mdpi.com/2076-3417/11/9/4214

Renewable energy forecasts are critical to renewable energy grids and backup plans, operational plans, and short-term power purchases. This paper focused on short-term forecasting of high-frequency global horizontal irradiance data from one of South Africa’s radiometric stations. The aim of the study was to compare the predictive performance of the genetic algorithm and recurrent neural network models with the K-nearest neighbour model, which was used as the benchmark model. Empirical results from the study showed that the genetic algorithm model has the best conditional predictive ability compared to the other two models, making this study a useful tool for decision-makers and system operators in power utility companies. To the best of our knowledge this is the first study which compares the genetic algorithm, the K-nearest neighbour method, and recurrent neural networks in short-term forecasting of global horizontal irradiance data from South Africa.