A data decomposition and attention mechanism-based hybrid approach for electricity load forecasting.

An accurate and reliable prediction of future energy patterns is of utmost significance for the smooth operation of several related activities such as capacity or generation unit planning, transmission network optimization, better resources availability, and many more. With the availability of historical load datasets through smart grid systems, artificial intelligence and machine learning-based techniques have been extensively developed for achieving the desired objectives. However, effectively capturing strong randomness and non-linear fluctuations in the load time-series remains a critical issue that demands concrete solutions. Considering this, the current research proposes a hybrid approach amalgamating data smoothing and decomposition strategy with deep neural models for improving forecasting results. Moreover, an attention mechanism is integrated to capture relevant portions of the time series, thus achieving the desired ability to capture long-term dependencies among load demand observations. This integration enhances the prediction and generalization capabilities of the proposed model. To validate the performance benefits achieved by the proposed approach, a comparative evaluation is conducted with state-of-the-art neural-based load series prediction models. The performance assessment is carried out on a novel real-world dataset of five southern states of India, and the superiority of the proposed in capturing load time-series variations is well observed and demonstrated in terms of several performance indicators. [ABSTRACT FROM AUTHOR]