In recent years, the importance of reducing carbon dioxide (CO2) emissions has increased. With the use of technologies such as artificial intelligence, we can improve the way households manage their energy use to decrease cost and carbon emissions. In this paper, we use the Spectral Entropy and Instantaneous Frequency-based Bidirectional Long Short Term Memory (SE-IF BiLSTM) method so the Home Energy Management System (HEMS) can learn from historical use data of energy, as well as the preferred consumption patterns for the user. With this data, a multi-objective optimisation problem (MOP) that considers cost, CO2 emissions and discomfort is generated to schedule appliances in different simulation scenarios. These scenarios include households with Battery Storage Systems and with or without Renewable Energy Sources (RES). We compare the results by using Multi-objective Immune Algorithm (MOIA) where we find a 10.06% reduction in cost and 20.56% reduction in CO2 emissions when scheduling the appliances, while minimising user discomfort.
Correa-Delval, M., Sun, H., Matthews, P. C., & Chiu, W. (2022). Appliance Scheduling Optimisation Method Using Historical Data in Households with RES Generation and Battery Storage Systems. . https://doi.org/10.1109/repe55559.2022.9949497