GA-ANN hybrid approach for load forecasting. Issue 1 (2nd January 2020)

• Record Type: Journal Article
• Authors: Sudha, K.; Kumar, Neeraj; Khetarpal, Poras
• Is Part Of: Journal of statistics & management systems. Volume 23:Issue 1(2020)
• Page Start: 135
• Page End: 144
• Publication Date: 2020-01-02
• Subjects: 92B20; Load Forecasting -- GA-NN -- NARX -- Neural Network -- Load Prediction -- Mean Absolute Percentage Error; Statistics -- Periodicals; Mathematical models -- Periodicals; Mathematical models; Statistics; Periodicals; 519.5
• DOI: 10.1080/09720510.2020.1714155
• Languages: English
• ISSNs: 0972-0510

Abstract

Abstract: Precise Load forecasting holds an incredible valid prospective to foretell energy expected, which fulfils the need and supply balance. It is consequently vital that the power producing establishments have preceding information on future demand with incredible accuracy. There are different strategies available for load forecasting. This research paper surveys and investigates the usage of Genetic Algorithm based Neural Networks (GA-NN) method for the analysis of load demand and comparing the results Load and Price forecasting is an important factor because of the growing load demand, and this information is used to plan maintenance, capacity requirement and planning, which in turn improves efficiency and thus reduces costs of generation and transmission. Probabilistic forecasting is gaining great attention nowadays in forecasting of load and price of electricity.