Automated Short Term Load Prediction in Power Systems using Collision Bodies Optimization with MultiHead Deep Learning Model

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Short-term load forecasting (STLF) of power systems is an important portion of the daily dispatch of the power industry. The preciseness of STLF straightforwardly disturbs the reliability, security, and economy of power system function. Thus, the research on STLF techniques is the main focus of researchers at abroad and home. Recently, artificial neural networks (ANN) were broadly studied as an intellectual method and implemented in the domain of short-term power load forecasting. Distinct methods like hybrid, conventional, and Artificial Intelligence (AI) methods were advanced to examine STLF. In this view, this study develops an Automated Short Term Load Prediction in Power Systems using Collision Bodies Optimization with MultiHead Deep Learning (AS TLP-CBMDL) model. The major intention of the AS TLP-CBMDL methodology is to predict the load in power systems which are adaptable to the time varying characteristics. To accomplish this, the ASTLP-CBMDL system model applies multihead attention based long short-term memory (MHALS TM) technique for performing load prediction. In addition, the colliding body's optimization (CBO) algorithm is utilized to optimally tune the hyperparameters related to the MHALSTM model to enhance the prediction efficacy. The experimental validation of the ASTLP-CBMDL model is tested using open access dataset and the outcomes are examined extensively. The comprehensive result analysis stated the enhanced performance of the ASTLP-CBMDL model over recent approaches.