Distributed Machine Learning on Smart-Gateway Network toward Real-Time Smart-Grid Energy Management with Behavior Cognition
| Autor: | Yuehua Cai, Hang Xu, Hao Yu, Rai Suleman Khalid, Hantao Huang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
business.industry
Energy management Computer science 020209 energy Real-time computing 02 engineering and technology Computer Graphics and Computer-Aided Design Computer Science Applications Renewable energy Energy management system Smart grid Peak demand Indoor positioning system 0202 electrical engineering electronic engineering information engineering Data analysis 020201 artificial intelligence & image processing Electrical and Electronic Engineering business Energy (signal processing) |
| Zdroj: | ACM Transactions on Design Automation of Electronic Systems. 23:1-26 |
| ISSN: | 1557-7309 1084-4309 |
| Popis: | Real-time data analytics for smart-grid energy management is challenging with consideration of both occupant behavior profiles and energy profiles. This article proposes a distributed and networked machine-learning platform on smart-gateway-based smart-grid in residential buildings. It can analyze occupant behaviors, provide short-term load forecasting, and allocate renewable energy resources. First, occupant behavior profile is captured by real-time indoor positioning system with WiFi data analytics; and the energy profile is extracted by real-time meter system with electricity load data analytics. Then, the 24-hour occupant behavior profile and energy profile are fused with prediction using an online distributed machine-learning algorithm with real-time data update. Based on the forecasted occupant behavior profile and energy profile, solar energy source is allocated to reduce peak demand on the main electricity power-grid. The whole management flow can be operated on the distributed smart-gateway network with limited computational resources but with a supported general machine-learning engine. Experimental results on occupant behavior extraction show that the proposed algorithm can achieve 91.2% positioning accuracy within 3.64m. Moreover, 50× and 38× speed-up is obtained during data testing and training, respectively, when compared to traditional support vector machine (SVM) method. For short-term load forecasting, it is 14.83% more accurate when compared to SVM-based data analytics. Based on the predicted occupant behavior profile and energy profile, our proposed energy management system can achieve 19.66% more peak load reduction and 26.41% more cost saving as compared to the SVM-based method. |
| Databáze: | OpenAIRE |
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