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A platform for research: civil engineering, architecture and urbanism
Multi-model prediction and simulation of residential building energy in urban areas of Chongqing, South West China
Highlights Residential building energy use has been simulated at Chongqing urban areas. Energy forecasting has been made by various models. A comparison of energy scenario prediction has been done among the models. Statistical precision has been carried out for accuracy of prediction and models.
Abstract Energy simulation and prediction plays a vital role in energy policy and decision making. This study has been conducted to predict the future energy demand in the urban residential buildings of Chongqing a city in south west China. The comparative study adopts and compares the results of different demand models to improve estimation efficiency for future projections. A structured questionnaire survey was undertaken to collect primary household energy consumption data for inclusion in the annual energy consumption simulation model. An ANN model, two Grey models, a Regression model, a Polynomial model and a Polynomial regression model were used to forecast and compare demand. The precision of the models have been used statistical methods. The predicted results show that the total residential building energy and electricity consumption in urban areas of Chongqing is increasing rapidly. Based on MRPE (%) and the statistical tests, the study concluded that an ANN model is the most acceptable forecasting method of the six models. Hence, based on ANN model, urban residential building energy consumption will be at 1005×104 SCE and electricity consumption will be at 264.81×108 kWh in 2025 which is about three times and four times higher than that of the 2012, respectively.
Multi-model prediction and simulation of residential building energy in urban areas of Chongqing, South West China
Highlights Residential building energy use has been simulated at Chongqing urban areas. Energy forecasting has been made by various models. A comparison of energy scenario prediction has been done among the models. Statistical precision has been carried out for accuracy of prediction and models.
Abstract Energy simulation and prediction plays a vital role in energy policy and decision making. This study has been conducted to predict the future energy demand in the urban residential buildings of Chongqing a city in south west China. The comparative study adopts and compares the results of different demand models to improve estimation efficiency for future projections. A structured questionnaire survey was undertaken to collect primary household energy consumption data for inclusion in the annual energy consumption simulation model. An ANN model, two Grey models, a Regression model, a Polynomial model and a Polynomial regression model were used to forecast and compare demand. The precision of the models have been used statistical methods. The predicted results show that the total residential building energy and electricity consumption in urban areas of Chongqing is increasing rapidly. Based on MRPE (%) and the statistical tests, the study concluded that an ANN model is the most acceptable forecasting method of the six models. Hence, based on ANN model, urban residential building energy consumption will be at 1005×104 SCE and electricity consumption will be at 264.81×108 kWh in 2025 which is about three times and four times higher than that of the 2012, respectively.
Multi-model prediction and simulation of residential building energy in urban areas of Chongqing, South West China
Farzana, Shazia (author) / Liu, Meng (author) / Baldwin, Andrew (author) / Hossain, Md. Uzzal (author)
Energy and Buildings ; 81 ; 161-169
2014-06-04
9 pages
Article (Journal)
Electronic Resource
English
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