Smart grid data analytics framework for increasing energy savings in residential buildings

Abstract Human energy consumption has gradually increased greenhouse gas concentrations and is considered the main cause of global warming. Currently, the building sector is a major energy consumer, and its share of energy consumption is increasing because of urbanization. This paper presents a framework for smart grid big data analytics and components required for an energy-saving decision-support system. The proposed system has a layered architecture that includes a smart grid, a data collection layer, an analytics bench, and a web-based portal. A smart metering infrastructure was installed in a residential building to conduct an experiment for evaluating the effectiveness of the proposed framework. Furthermore, a novel hybrid nature-inspired metaheuristic forecast system and a dynamic optimization algorithm are designed behind the analytics bench for achieving accurate prediction and optimization of future energy consumption. The main contribution of this study is that an innovative framework for the energy-saving decision process is presented; the framework can serve as a basis for the future development of a full-scale smart decision support system (SDSS). Through the identification of consumer usage patterns, the SDSS is expected to enhance energy use efficiency and improve the accuracy of future energy demand estimates. End users can reduce their electricity costs by implementing the optimal operating schedules for appliances, which are provided by the SDSS.

[1]  Chin-Feng Lai,et al.  A Smart Appliance Management System with Current Clustering Algorithm in Home Network , 2011, GreeNets.

[2]  Zita Vale,et al.  A data-mining-based methodology to support MV electricity customers’ characterization , 2015 .

[3]  A. C. Davies.  An overview of Bluetooth Wireless Technology/sup TM/ and some competing LAN standards , 2002, ICCSC'02. 1st IEEE International Conference on Circuits and Systems for Communications. Proceedings (IEEE Cat. No.02EX605).

[4]  Frédéric Magoulès,et al.  A review on the prediction of building energy consumption , 2012 .

[5]  Fang Xu,et al.  Energy demand forecasting of the greenhouses using nonlinear models based on model optimized prediction method , 2016, Neurocomputing.

[6]  Yi Zhang,et al.  Energy conservation co-operative control mechanism based on gray prediction for structural air compressor , 2013 .

[7]  Roselina Sallehuddin,et al.  Hybrid Support Vector Regression and Autoregressive Integrated Moving Average Models Improved by Particle Swarm Optimization for Property Crime Rates Forecasting with Economic Indicators , 2013, TheScientificWorldJournal.

[8]  Chatschik Bisdikian,et al.  An overview of the Bluetooth wireless technology , 2001, IEEE Commun. Mag..

[9]  Matthias Wissner,et al.  The Smart Grid – A saucerful of secrets? , 2011 .

[10]  Dong-Joo Kang,et al.  Proposal strategies of key management for data encryption in SCADA network of electric power systems , 2009 .

[11]  Da-yong Zhang,et al.  Stock market forecasting model based on a hybrid ARMA and support vector machines , 2008, 2008 International Conference on Management Science and Engineering 15th Annual Conference Proceedings.

[12]  Ying Zhang,et al.  Research on energy saving control strategy of parallel hybrid loader , 2014 .

[13]  Marcus M. Keane,et al.  A performance assessment ontology for the environmental and energy management of buildings , 2015 .

[14]  Mohamed E. El-Hawary,et al.  The Smart Grid—State-of-the-art and future trends , 2014, 2016 Eighteenth International Middle East Power Systems Conference (MEPCON).

[15]  Z. Vale,et al.  Demand response in electrical energy supply: An optimal real time pricing approach , 2011 .

[16]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[17]  Paul Raftery,et al.  Key factors methodology—A novel support to the decision making process of the building energy manager in defining optimal operation strategies , 2012 .

[18]  Ping-Feng Pai,et al.  A hybrid ARIMA and support vector machines model in stock price forecasting , 2005 .

[19]  Jianzhou Wang,et al.  Short-term electricity prices forecasting based on support vector regression and Auto-regressive integrated moving average modeling , 2010 .

[20]  Myungdo Lee,et al.  Green construction hoist with customized energy regeneration system , 2014 .

[21]  F. J. D. C. Juez,et al.  Forecasting the COMEX copper spot price by means of neural networks and ARIMA models , 2015 .

[22]  Ratnadip Adhikari,et al.  Time Series Forecasting Using Hybrid ARIMA and ANN Models Based on DWT Decomposition , 2015 .

[23]  Carlos Alberto Fróes Lima,et al.  Smart metering and systems to support a conscious use of water and electricity , 2012 .

[24]  James V. Hansen,et al.  Time-series analysis with neural networks and ARIMA-neural network hybrids , 2003, J. Exp. Theor. Artif. Intell..

[25]  Lingfeng Wang,et al.  Smart meters for power grid — Challenges, issues, advantages and status , 2011, 2011 IEEE/PES Power Systems Conference and Exposition.

[26]  Yu-Hsiu Lin,et al.  Modern development of an Adaptive Non-Intrusive Appliance Load Monitoring system in electricity energy conservation , 2012 .

[27]  Nadeem Javaid,et al.  A review of wireless communications for smart grid , 2015 .

[28]  Claudio Gallicchio,et al.  Prediction of the Italian electricity price for smart grid applications , 2015, Neurocomputing.

[29]  Zhaoguang Hu,et al.  Low carbon electricity development in China—An IRSP perspective based on Super Smart Grid , 2011 .

[30]  F. Magnago,et al.  Impact of demand response resources on unit commitment and dispatch in a day-ahead electricity market , 2015 .

[31]  Xin-She Yang,et al.  Analysis of Algorithms , 2021, Nature-Inspired Optimization Algorithms.

[32]  Siti Mariyam Hj. Shamsuddin,et al.  HYBRID GREY RELATIONAL ARTIFICIAL NEURAL NETWORK AND AUTO REGRESSIVE INTEGRATED MOVING AVERAGE MODEL FOR FORECASTING TIME-SERIES DATA , 2009, Appl. Artif. Intell..

[33]  Chiara Aghemo,et al.  Building automation and control systems: A case study to evaluate the energy and environmental performances of a lighting control system in offices , 2014 .

[34]  Asunción Gómez-Pérez,et al.  Guidelines for Linked Data generation and publication: An example in building energy consumption , 2015 .

[35]  Jui-Sheng Chou,et al.  Real-time detection of anomalous power consumption , 2014 .

[36]  Tarik Kousksou,et al.  Energy consumption and efficiency in buildings: current status and future trends , 2015 .

[37]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[38]  Marcelo Saguan,et al.  Assessing long-term effects of demand response policies in wholesale electricity markets , 2016 .

[39]  Mehdi Khashei,et al.  A novel hybridization of artificial neural networks and ARIMA models for time series forecasting , 2011, Appl. Soft Comput..

[40]  Milind Tambe,et al.  Coordinating occupant behavior for building energy and comfort management using multi-agent systems , 2012 .

[41]  Lianjun An,et al.  Applying science and mathematics to big data for smarter buildings , 2013, Annals of the New York Academy of Sciences.

[42]  Kevin J. Lomas,et al.  Identifying trends in the use of domestic appliances from household electricity consumption measurements , 2008 .

[43]  Stéphane Ploix,et al.  Prediction of appliances energy use in smart homes , 2012 .

[44]  Xiao-Ping Zhang,et al.  Real-time Energy Control Approach for Smart Home Energy Management System , 2014 .

[45]  Wolfgang Kastner,et al.  ThinkHome Energy Efficiency in Future Smart Homes , 2011, EURASIP J. Embed. Syst..

[46]  Jieh-Haur Chen,et al.  Developing an SVM based risk hedging prediction model for construction material suppliers , 2010 .

[47]  Guoqiang Peter Zhang,et al.  Time series forecasting using a hybrid ARIMA and neural network model , 2003, Neurocomputing.

[48]  Valeriy Vyatkin,et al.  Energy Efficient Agent Function Block: A semantic agent approach to IEC 61499 function blocks in energy efficient building automation systems , 2015 .

[49]  Yongcai Wang,et al.  Smart meter deployment optimization for efficient electrical appliance state monitoring , 2012, 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm).

[50]  Evgueniy Entchev,et al.  Adaptive Neuro-Fuzzy Inference System modelling for performance prediction of solar thermal energy system , 2016 .

[51]  Katia Gregio Di Santo,et al.  A review on smart grids and experiences in Brazil , 2015 .