Intelligent IoT-Based Home Energy Management System using BO-BiLSTM

Cost savings, environmental protection, and a smaller carbon footprint are all benefits of smart houses with efficient energy consumption. As a result, Intelligent Home Energy Management Systems are vital for smart city and smart home energy efficiency optimisation. Unfortunately, there aren't many low-cost, easy-to-implement, and low-maintenance solutions available, which is preventing their widespread adoption. There are also difficulties in storing, managing, and analysing the massive amounts of data produced from many sources. Big Data and the IoT provide scalable answers to these problems by allowing us to sense, monitor, and control the energy consumption in our homes. Preprocessing, feature extraction, and model training are the three steps that make up the proposed Intelligent Home Energy Management System. Before offline feature extraction and grouping, normalisation is used as a preprocessing step. BO-BiLSTM is used to train the model, which improves the accuracy of predictions. With better efficiency and scalability, the suggested method beats the standard BiLSTM and BO-LSTM models, according to the results. The system's integration of IoT and Big Data allows it to more reliably regulate energy use and successfully overcome deployment hurdles. Improving computing efficiency and integrating real-time adaptive learning for additional optimisation can be the subject of future study.