An Optimum Load Forecasting Strategy (OLFS) for Smart Grids Based on Artificial Intelligence

• Published in: Technologies
• Main Authors: Asmaa Hamdy Rabie, Ahmed I. Saleh, Said H. Abd Elkhalik, Ali E. Takieldeen
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2024
• Subjects: artificial intelligence; load forecasting; feature selection; outlier rejection; Technology; T; Leopard Seal
• Online Access: https://doi.org/10.3390/technologies12020019; https://doaj.org/article/79ce5d00b96c45648abd1e5f753c31c3

Recently, the application of Artificial Intelligence (AI) in many areas of life has allowed raising the efficiency of systems and converting them into smart ones, especially in the field of energy. Integrating AI with power systems allows electrical grids to be smart enough to predict the future load, which is known as Intelligent Load Forecasting (ILF). Hence, suitable decisions for power system planning and operation procedures can be taken accordingly. Moreover, ILF can play a vital role in electrical demand response, which guarantees a reliable transitioning of power systems. This paper introduces an Optimum Load Forecasting Strategy (OLFS) for predicting future load in smart electrical grids based on AI techniques. The proposed OLFS consists of two sequential phases, which are: Data Preprocessing Phase (DPP) and Load Forecasting Phase (LFP). In the former phase, an input electrical load dataset is prepared before the actual forecasting takes place through two essential tasks, namely feature selection and outlier rejection. Feature selection is carried out using Advanced Leopard Seal Optimization (ALSO) as a new nature-inspired optimization technique, while outlier rejection is accomplished through the Interquartile Range (IQR) as a measure of statistical dispersion. On the other hand, actual load forecasting takes place in LFP using a new predictor called the Weighted K-Nearest Neighbor (WKNN) algorithm. The proposed OLFS has been tested through extensive experiments. Results have shown that OLFS outperforms recent load forecasting techniques as it introduces the maximum prediction accuracy with the minimum root mean square error.