Publication Type:

Conference Paper

Source:

CEUR Workshop Proceedings, CEUR-WS, Volume 1819 (2017)

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017474506&partnerID=40&md5=740c04fe30df924d8d25bd0f7a949041

Keywords:

Adaptability, Adaptive systems, Artifical intelligence, Automation, Conformal mapping, Data mining, Deep neural networks, Demand prediction, Electric power utilization, Electricity-consumption, Energy efficiency, Energy management, Energy management strategies, Energy utilization, Environmental factors, Environmental impact, Forecasting, Fuzzy inference, Fuzzy logic, Fuzzy neural networks, Hybrid intelligent system, Intelligent buildings, Intelligent systems, Learning systems, Neural networks, Non-linear relationships, Self organizing maps, Software engineering, Wind

Abstract:

<p>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 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 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. Copyright ©2017 for the individual papers by the papers' authors.</p>

Notes:

cited By 0; Conference of Joint 3rd Modelling Symposium, ModSym 2017, Developmental Aspects of Intelligent Adaptive Systems, DIAS 2017, and Educational Data Mining Practices in Indian Academia, EDUDM 2017 ; Conference Date: 5 February 2017; Conference Code:126941

Cite this Research Publication

J. Jithish and Sankaran, S., “A Hybrid Adaptive Rule Based System For Smart Home Energy Prediction”, in CEUR Workshop Proceedings, 2017, vol. 1819.

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