DB-Net: A novel dilated CNN based multi-step forecasting model for power consumption in integrated local energy systems

• A Novel multi-step forecasting model for power consumption. • Dilated convolutional neural network and bidirectional LSTM based hybrid network model. • Deep learning based smart energy management for integrated local energy systems. • Comparative analysis of hybrid CNN and RNN models with traditional machine learning approaches. In the era of cutting edge technology, excessive demand for electricity is rising day by day, due to the exponential growth of population, electricity reliant vehicles, and home appliances. Precise energy consumption prediction (ECP) and integrated local energy systems (ILES) are critical to boost clean energy management systems between consumers and suppliers. Various obstacles such as environmental factors and occupant behavior affects the performance of existing approaches for long- and short-term ECP. Thus, to address such concerns, we present a novel hybrid network model ‘DB-Net’ by incorporating a dilated convolutional neural network (DCNN) with bidirectional long short-term memory (BiLSTM). The proposed approach allows efficient control of power energy in ILES between consumer and supplier when employed for long- and short-term ECP. The first phase combines data acquisition and refinement procedures into a preprocessing module in which the main goal is to optimize the collected data and to handle outliers. In the next phase, the refined data is passed into DCNN layers for feature encoding followed by BiLSTM layers to learn hidden sequential patterns and decode the feature maps. In the final phase, the DB-Net model forecasts multi-step power consumption (PC), including hourly, daily, weekly, and monthly output. The proposed approach attains better predictive performance than existing methods, thereby confirming its effectiveness.