A Hybrid Adaptive Rule based System for Smart Home Energy Prediction

The increase in energy prices combined with the environmental impact of energy production has made energy efficiency a key component towards the development of smart homes. An efficient energy management strategy for smart homes results in minimized electricity consumption leading to cost savings. Towards this goal, we investigate the impact of environmental factors on home energy consumption. Home energy demand is observed to be affected by environmental factors such as temperature, wind speed, and humidity which are inherently uncertain. Analyzing the impact of these factors on electricity consumption is challenging due to the unpredictability of weather conditions and the non-linear relationship between environmental factors and electricity demand. For demand estimation based on these time-varying factors, a hybrid intelligent system is developed that integrates the adaptability of neural networks and the reasoning of fuzzy systems to predict daily electricity demand. A smart home dataset is utilized to build an unsupervised artificial neural network known as the Self-Organizing Map (SOM). We further develop a fuzzy rule-based system from the SOM to predict home energy demand. Evaluation of the system shows a strong correlation between home energy demand and environmental factors and that the system predicts home energy consumption with higher accuracy.

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Recommended citation: J Jithish, Sriram Sankaran. (2017). “A Hybrid Adaptive Rule based System for Smart Home Energy Prediction.” DIAS/EDUDM@ ISEC.