首页|Study Findings on Artificial Intelligence Discussed by a Researcher at Mansoura University [An Optimum Load Forecasting Strategy (OLFS) for Smart Grids Based on Artificial Intelligence]

Study Findings on Artificial Intelligence Discussed by a Researcher at Mansoura University [An Optimum Load Forecasting Strategy (OLFS) for Smart Grids Based on Artificial Intelligence]

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Current study results on artificial intelligence have been published. According to news reporting from Mansoura, Egypt, by NewsRx journalists, research stated, “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).” Our news reporters obtained a quote from the research from Mansoura University: “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.”

Mansoura UniversityMansouraEgyptAfricaArtificial IntelligenceEmerging TechnologiesMachine Learning

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Feb.19)
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