Development of Prediction Models of Day-Ahead Hourly Building Electricity Consumption and Peak Power Demand Using the Machine Learning Method
To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control.
[1] "Energy Efficiency." Energy Efficiency. Accessed May 03, 2016. http://www.iea.org/aboutus/faqs/energyefficiency/.
[2] Wattles, Paul. "ERCOT demand response overview & status report." In AMIT-DSWG Workshop “AMI’s Next Frontier: Demand Response. 2011.
[3] Borenstein, Severin, Michael Jaske, and Arthur Rosenfeld. "Dynamic pricing, advanced metering, and demand response in electricity markets." (2002).
[4] Cochran, Jaquelin, Mackay Miller, Michael Milligan, Erik Ela, Douglas Arent, Aaron Bloom, Matthew Futch et al. "Market evolution: wholesale electricity market design for 21st century power systems." Contract (2013).
[5] Patel, Siddharth, Raffi Sevlian, Baosen Zhang, and Ram Rajagopal. "Pricing Residential Electricity Based on Individual Consumption Behaviors." arXiv preprint arXiv:1312.1243 (2013).
[6] Yoon, Ji Hoon, Ross Bladick, and Atila Novoselac. "Demand response for residential buildings based on dynamic price of electricity." Energy and Buildings 80 (2014): 531-541.
[7] Zhao, Hai-xiang, and Frédéric Magoulès. "A review on the prediction of building energy consumption." Renewable and Sustainable Energy Reviews 16, no. 6 (2012): 3586-3592.
[8] Dong, Bing, Cheng Cao, and Siew Eang Lee. "Applying support vector machines to predict building energy consumption in tropical region." Energy and Buildings 37, no. 5 (2005): 545-553.
[9] Yokoyama, Ryohei, Tetsuya Wakui, and Ryoichi Satake. "Prediction of energy demands using neural network with model identification by global optimization." Energy Conversion and Management 50, no. 2 (2009): 319-327.
[10] QDR, Q. "Benefits of demand response in electricity markets and recommendations for achieving them." US Dept. Energy, Washington, DC, USA, Tech. Rep (2006).
[11] Barbose, Galen, Charles Goldman, and Bernie Neenan. "A survey of utility experience with real time pricing." Lawrence Berkeley National Laboratory (2004).
[12] Federal Energy Regulatory Commission. "Assessment of demand response and advanced metering." (2012).
[13] "Training Material." PJM - Accessed May 03, 2016. http://pjm.com/training/training-material.aspx.
[14] "1.4. Support Vector Machines." 1.4. Support Vector Machines — Scikit-learn 0.17.1 Documentation. Accessed May 03, 2016. http://scikit-learn.org/stable/modules/svm.html#svr.
[15] "Decision Tree Regression with AdaBoost." Decision Tree Regression with AdaBoost — Scikit-learn 0.17.1 Documentation. Accessed May 03, 2016. http://scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_regression.html.
[16] "PyBrain Documentation." PyBrain. Accessed May 03, 2016. http://pybrain.org/.
[17] Documentation, EnergyPlus. "Engineering reference." (2005).
[18] "V2 Forecast API." The Dark Sky Forecast API Docs. Accessed May 03, 2016. https://developer.forecast.io/docs/v2.
[19] Zhao, Jie. "Design-Build-Operate Energy Information Modeling for Occupant-Oriented Predictive Building Control." (2015).
[20] Li, Qiong, Peng Ren, and Qinglin Meng. "Prediction model of annual energy consumption of residential buildings." In Advances in Energy Engineering (ICAEE), 2010 International Conference on, pp. 223-226. IEEE, 2010.
[21] Guideline, A. S. H. R. A. E. "Guideline 14-2002, Measurement of Energy and Demand Savings." American Society of Heating, Ventilating, and Air Conditioning Engineers, Atlanta, Georgia (2002).
[22] "Data Miner - Locational Marginal Pricing." Data Miner - Locational Marginal Pricing. Accessed May 03, 2016. https://dataminer.pjm.com/dataminerui/pages/public/lmp.jsf.
[1] "Energy Efficiency." Energy Efficiency. Accessed May 03, 2016. http://www.iea.org/aboutus/faqs/energyefficiency/.
[2] Wattles, Paul. "ERCOT demand response overview & status report." In AMIT-DSWG Workshop “AMI’s Next Frontier: Demand Response. 2011.
[3] Borenstein, Severin, Michael Jaske, and Arthur Rosenfeld. "Dynamic pricing, advanced metering, and demand response in electricity markets." (2002).
[4] Cochran, Jaquelin, Mackay Miller, Michael Milligan, Erik Ela, Douglas Arent, Aaron Bloom, Matthew Futch et al. "Market evolution: wholesale electricity market design for 21st century power systems." Contract (2013).
[5] Patel, Siddharth, Raffi Sevlian, Baosen Zhang, and Ram Rajagopal. "Pricing Residential Electricity Based on Individual Consumption Behaviors." arXiv preprint arXiv:1312.1243 (2013).
[6] Yoon, Ji Hoon, Ross Bladick, and Atila Novoselac. "Demand response for residential buildings based on dynamic price of electricity." Energy and Buildings 80 (2014): 531-541.
[7] Zhao, Hai-xiang, and Frédéric Magoulès. "A review on the prediction of building energy consumption." Renewable and Sustainable Energy Reviews 16, no. 6 (2012): 3586-3592.
[8] Dong, Bing, Cheng Cao, and Siew Eang Lee. "Applying support vector machines to predict building energy consumption in tropical region." Energy and Buildings 37, no. 5 (2005): 545-553.
[9] Yokoyama, Ryohei, Tetsuya Wakui, and Ryoichi Satake. "Prediction of energy demands using neural network with model identification by global optimization." Energy Conversion and Management 50, no. 2 (2009): 319-327.
[10] QDR, Q. "Benefits of demand response in electricity markets and recommendations for achieving them." US Dept. Energy, Washington, DC, USA, Tech. Rep (2006).
[11] Barbose, Galen, Charles Goldman, and Bernie Neenan. "A survey of utility experience with real time pricing." Lawrence Berkeley National Laboratory (2004).
[12] Federal Energy Regulatory Commission. "Assessment of demand response and advanced metering." (2012).
[13] "Training Material." PJM - Accessed May 03, 2016. http://pjm.com/training/training-material.aspx.
[14] "1.4. Support Vector Machines." 1.4. Support Vector Machines — Scikit-learn 0.17.1 Documentation. Accessed May 03, 2016. http://scikit-learn.org/stable/modules/svm.html#svr.
[15] "Decision Tree Regression with AdaBoost." Decision Tree Regression with AdaBoost — Scikit-learn 0.17.1 Documentation. Accessed May 03, 2016. http://scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_regression.html.
[16] "PyBrain Documentation." PyBrain. Accessed May 03, 2016. http://pybrain.org/.
[17] Documentation, EnergyPlus. "Engineering reference." (2005).
[18] "V2 Forecast API." The Dark Sky Forecast API Docs. Accessed May 03, 2016. https://developer.forecast.io/docs/v2.
[19] Zhao, Jie. "Design-Build-Operate Energy Information Modeling for Occupant-Oriented Predictive Building Control." (2015).
[20] Li, Qiong, Peng Ren, and Qinglin Meng. "Prediction model of annual energy consumption of residential buildings." In Advances in Energy Engineering (ICAEE), 2010 International Conference on, pp. 223-226. IEEE, 2010.
[21] Guideline, A. S. H. R. A. E. "Guideline 14-2002, Measurement of Energy and Demand Savings." American Society of Heating, Ventilating, and Air Conditioning Engineers, Atlanta, Georgia (2002).
[22] "Data Miner - Locational Marginal Pricing." Data Miner - Locational Marginal Pricing. Accessed May 03, 2016. https://dataminer.pjm.com/dataminerui/pages/public/lmp.jsf.
@article{"International Journal of Electrical, Electronic and Communication Sciences:74716", author = "Dalin Si and Azizan Aziz and Bertrand Lasternas", title = "Development of Prediction Models of Day-Ahead Hourly Building Electricity Consumption and Peak Power Demand Using the Machine Learning Method", abstract = "To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control.
", keywords = "Building energy prediction, data mining, demand response, electricity market.", volume = "11", number = "2", pages = "154-13", }
