Collaborative distributed prediction model based on decentralized federated learning for efficient energy resources utilization

Forecasting domestic energy needs is crucial to ensure a reliable and affordable energy supply, which is vital for maintaining economic stability and promoting growth. It also aids in planning and managing resources efficiently, reducing the risk of energy shortages and price volatility. The traditional literature presents numerous deep learning-inspired energy forecasting frameworks. However, this field may still be limited by various issues, as most frameworks focus solely on predicting short-term or long-term energy consumption. The research studies that have attempted to predict both long-term and short-term energy simultaneously may have failed to achieve optimal results concurrently, in terms of both high accuracy and low error. This research work presents a novel hybrid framework that combines long short-term memory (LSTM), convolutional neural network (CNN), and bi-directional long short-term memory (Bi-LSTM) in a distributed federated learning setup. This framework constitutes a simple yet effective predictive model designed to simultaneously forecast both immediate (short-term) and sustained (long-term) energy consumption. It harnesses the capability of CNN for local and spatial feature extraction. Subsequently, LSTM and Bi-LSTM are utilized for capturing current, past, and future contexts. The proposed model achieved an mean absolute percentage error (MAPE) score of 1.39. It achieves high accuracies in the simultaneous prediction of short and long-term energy, outperforming similar techniques in the literature for hourly, daily, weekly, and monthly energy consumption, with minimal computational costs.