Multivariate Singular Spectrum Analysis with Hybrid Optimization for Reliable Electricity Load Forecasting

Abstract

Nowadays, forecasting the electricity load plays a significant part in influencing daily electrical operations, including supply and demand decisions and smart grid resource planning. Optimal energy management, improved grid stability, and assistance for renewable energy integration all depend on accurate power load forecasts. Accuracy and efficiency are diminished when dealing with high-dimensional, noisy data using traditional forecasting methods. Hence, the research introduces a Hybridized Electricity Load Forecasting (HELF) model to boost the precision and reliability of load prediction in smart grid environments.  The model pre-processes historical electricity load data to extract key patterns and minimize noise. After that, an optimized machine learning model is used for feature selection and dimensionality reduction, which helps to mitigate overfitting and improve computational efficiency. Finally, a deep learning- assisted convolutional neural network is used for load forecasting, achieving a notable decrease in execution time and computation complexity. Experiments conducted on real-world smart grid datasets show that prediction accuracy is 15% higher and calculation time is 20% lower than traditional methods. Smart grids, renewable energy integration, and demand-response systems are just a few areas that stand to benefit significantly from this framework's promise of efficient and environmentally friendly electricity delivery.