Enhanced Automated Deep Learning Application for Short-Term Load Forecasting

In recent times, the power sector has become a focal point of extensive scientific interest, driven by convergence factors, such as mounting global concerns surrounding climate change, persistent increase in electricity prices within wholesale energy market, and surge investments catalyzed technological advancements across diverse sectors. These evolving challenges have necessitated emergence new imperatives aimed at effectively managing resources, ensuring grid stability, bolstering reliability, making informed decisions. One area that garnered particular attention is accurate prediction end-user load, which emerged critical facet pursuit efficient management. To tackle this challenge, machine deep learning models popular promising approaches, owing to their having remarkable effectiveness handling complex time series data. paper, development an algorithmic model leverages automated process provide highly predictions specifically tailored for island Thira Greece, introduced. Through implementation application, array forecasting were meticulously crafted, encompassing Multilayer Perceptron, Long Short-Term Memory (LSTM), Dimensional Convolutional Neural Network (CNN-1D), hybrid CNN–LSTM, Temporal (TCN), innovative called LSTM Encoder–Decoder. evaluation accuracy, satisfactory performance all considered was observed, with proposed showcasing highest level accuracy. findings underscore profound significance employing techniques precise demand, thereby offering valuable insights multifaceted encountered sector. By adopting advanced methodologies, moves towards greater efficiency, resilience sustainability.