Forecasting Electricity Load Demand- An Power System Planning

Abstract

Moving holiday electricity load demand forecasting is one of the most challenging topics in the forecasting area. Forecasting electricity load demand is essential because it involves projecting the peak demand level. Overestimation of future loads results in excess supply. Wastage of this load is not welcome by the international energy network. An underestimation of load leads to failure in providing adequate reserve, implying high costs. Many factors can influence the electricity load demand, such as previous load demand, type of the day, coincidence with other holidays and the impact of major events. Hence, 12 independent variables were considered in constructing the regression model to forecast moving holiday electricity load demand. This study investigates Malaysia’s daily electricity load demand data using multiple linear regression to forecast electricity load demand on moving holidays, such as Hari Raya AidilFitri, Chinese New Year, Hari Raya AidilAdha, and Deepavali from September 2016 to October 2017. The result shows six independent variables are significant from the several method variables selections. Overall, the constructed models from this study give promising results and can forecast for next year’s moving holiday electricity load demand with a sample forecasting error of 3.7% on the day of the moving holiday.

Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges

“…Clustering-based techniques streamline load forecasting by grouping similar load patterns, which aids in managing the variability from different energy sources and consumer behaviors. Techniques like K-means, hierarchical clustering, combined locally linear embedding (LLE), principal component analysis (PCA), and multi-layer perceptrons (MLPs), enhance accuracy, integrate renewable energy sources (RESs), aid demandside management (DSM), and bolster SG functions [15,[43][44][45][46]80]. Time series load forecasting (TSLF) methods utilize historical data, enhanced by advanced algorithms such as ARIMA and neural networks, to predict future demand.…”