Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 10.54216/JAIM https://www.americaspg.com/journals/show/4259 2022 2022 Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia Wei Wei Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia Amel Ali Alhussan Accurate prediction of electricity consumption is a critical requirement for improving operational efficiency, enhancing grid reliability, and supporting sustainability objectives in urban power distribution systems, particularly in regions experiencing steady population growth and increasing demand pressure. Motivated by the limitations of conventional statistical and physics-inspired forecasting approaches, as well as the strong sensitivity of deep learning architectures to hyperparameter configuration, t his s tudy p roposes a robust data-driven framework that integrates deep learning with advanced metaheuristic optimization for high-precision short-term electricity consumption forecasting. The main contribution of this work lies in the systematic development and evaluation of hybrid metaheuristic–Bidirectional Long Short-Term Memory (BiLSTM) models, in which multiple state-of-the-art optimization algorithms are employed to tune model hyperparameters. Particular emphasis is placed on the integration of the Ninja Optimization Algorithm with BiLSTM (NijOA + BiLSTM), which is designed to effectively navigate complex, high-dimensional hyperparameter search spaces encountered in deep learning–based load forecasting tasks. Baseline experiments demonstrate that BiLSTM outperforms other deep learning models, including Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), achieving a baseline Root Mean Squared Error (RMSE) of 0.0964 and a coefficient of determination (R2) of 0.854. These results confirm t he a dvantage o f b idirectional t emporal l earning in capturing the nonlinear and time-dependent characteristics of electricity consumption recorded at high temporal resolution from SCADA systems. Following metaheuristic optimization, the NijOA + BiLSTMmodel delivers a substantial improvement in predictive performance. The optimized configuration reduces RMSE to 0.0038, Mean Squared Error (MSE) to 1.45 × 10−5, and Mean Absolute Error (MAE) to 0.00019, while increasing the correlation strength to r = 0.973 and the explanatory power to R2 = 0.97. Comparative analysis across different optimization strategies further confirms t he s uperiority o f t he NijOA + BiLSTM hybrid model over alternative configurations, including WAO + BiLSTM, BBO + BiLSTM, GA + BiLSTM, SFS + BiLSTM, DE + BiLSTM, and JAYA + BiLSTM. The implications of these findings are significant for real-world urban electricity distribution applications. The proposed framework enables highly accurate and reliable short-term electricity consumption forecasting, making it well suited for deployment within smart grid and distribution management systems. Such predictive capability can support informed operational decision-making, improve demand-side management strategies, reduce uncertainty in short-term planning, and contribute to the long-term sustainability and resilience of urban power distribution networks. 2026 2026 01 28 10.54216/JAIM.110101 https://www.americaspg.com/articleinfo/28/show/4259