Real-Time Efficient Short-Term Peak Load and Day-Ahead Electricity Load Forecasting System Using Machine Learning Approach - S. Vasudevan (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Short-term load forecasting is crucial for efficiently managing electricity usage, spanning from weekly down to sub-hourly intervals. This practice not only saves resources but also ensures customer needs are met promptly. Day-ahead peak demand forecasting plays a vital role in load management, aiding in power system planning and operation. Yet, due to its complex non-linear nature, accurately predicting peak loads presents significant challenges. Therefore, this research proposed a hybrid predictive deep learning with an optimization algorithm to forecast precise electricity for 30-minute intervals. First, the maximum and minimum ranges of various parameters are determined by looking at historical data. The real-time datasets from January 1 to December 31st 2021 were used to derive hourly real-time system demand load data. The original dataset undergoes preprocessing to reconstruct its electrical characteristics. Zero-mean normalization is applied to both load and temperature data to standardize them. In intricate electric load systems, redundant information can hinder accurate pattern extraction for load forecasting. Principal Component Analysis Network (PCANet) identifies relevant features while eliminating redundancy. A DeepWalk Gated Recurrent Unit Model (DWGRU) framework is then constructed to capture temporal dependencies from historical sequences, integrating spatial, temporal, and semantic features for load forecasting. These extracted features are dynamically combined using an attention mechanism. Subsequently, a Hybrid Sampling and Self Attention with Deep Neural Network (HSSA-DNN) is employed to forecast 30-minute peak loads efficiently, utilizing Improved Moth Flame Optimization (IMFO). The proposed methodology is implemented using Matlab Simulink software. Forecasting accuracy is assessed using statistical error metrics such as Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) to identify optimal models. Experimental results showcase the superior accuracy of the proposed approach, evidenced by precise direction, equality, stability, correlation, comprehensive accuracy, and statistical performance analysis. Comparisons with existing methods reveal a reduction in Mean Absolute Scaled Error of up to 0.2088. Achieving a high day-ahead net load forecasting accuracy of 99.15% underscores the effectiveness of load forecasting. This highlights the critical role of input data structure and quality in further enhancing forecasting accuracy and reliability.