Appliance Scheduling Optimisation Method Using Historical Data in Households with RES Generation and Battery Storage Systems

Correa-Delval, Martha; Sun, Hongjian; Matthews, Peter C.; Chiu, Wei-Yu

Abstract

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.

Citation

Correa-Delval, M., Sun, H., Matthews, P. C., & Chiu, W.-Y. (2022, September). Appliance Scheduling Optimisation Method Using Historical Data in Households with RES Generation and Battery Storage Systems. Presented at 2022 5th International Conference on Renewable Energy and Power Engineering (REPE 2021), Beijing, China

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2022 5th International Conference on Renewable Energy and Power Engineering (REPE 2021)
• Start Date: Sep 28, 2022
• End Date: Sep 30, 2022
• Acceptance Date: Aug 30, 2022
• Online Publication Date: Nov 21, 2022
• Publication Date: 2022
• Deposit Date: Sep 5, 2022
• Publicly Available Date: Sep 22, 2022
• Publisher: Institute of Electrical and Electronics Engineers
• DOI: https://doi.org/10.1109/repe55559.2022.9949497
• Public URL: https://durham-repository.worktribe.com/output/1136248
• Related Public URLs: http://www.repe.net/