Load scheduling for smart home using day-ahead prediction from artificial neural network (ANN)

• Main Authors: Joharry, S. H., Hussin, S. M., Rosmin, N., M. Said, D.
• Format: Article
• Published: World Academy of Research in Science and Engineering 2020
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/id/eprint/90745/; http://dx.doi.org/10.30534/ijatcse/2020/9291.42020
• Institution: Universiti Teknologi Malaysia

This paper presents load scheduling for smart home application using day-ahead prediction from an artificial neural network (ANN). In this study, load forecasting using ANN approach is embedded in the load scheduling scheme that is modeled using mixed integer linear programming (MILP). The main objective of the scheduling is to reduce the electricity bill by shifting peak load to off-peak period. A day-ahead energy consumption is predicted based on a previous yearly data set of hourly resolution. The dataset is normalized and injected as input in ANN and the result is then fed to the load scheduling optimization process. The results show that the integration process affects the allocation of load consumption in the load profile as well as the electricity cost. From the comparative study between before and after ANN integration, the total cost saving achieved is $1.53/day with the cost reduction of 38.44%.