ANN-GA smart appliance scheduling for optimised energy management in the domestic sector

作者：Yuce Baris^*; Rezgui Yacine; Mourshed Monjur

来源：Energy and Buildings, 2016, 111: 311-325.

DOI：10.1016/j.enbuild.2015.11.017

• ANN
• Optimisation, Genetic Algorithm
• Scheduling
• Energy management
• Parameter tuning
• PMV
• Domestic building

Smart scheduling of energy consuming devices in the domestic sector should factor in clean energy generation potential, electricity tariffs, and occupants' behaviour (i.e., interactions with their appliances). The paper presents an Artificial Neural Network/Genetic Algorithm (ANN-GA) smart appliance scheduling approach for optimised energy management in the domestic sector. The proposed approach reduces energy demand in "peak" periods, maximises use of renewable sources (PV and wind turbine), while reducing reliance on grid energy. Comprehensive parameter optimisation has been carried out for both ANN and GA to find the best combinations, resulting in optimum weekly schedules. The proposed artificial intelligence techniques involve a holistic understanding of (near) real-time energy demand and supply within a domestic context to deliver optimised energy usage with minimum computational needs. The solution is stress-tested and demonstrated in a four bedroom house with grid energy usage reduction by 10%, 25%, and 40%, respectively.

• 出版日期2016-1-1