One of the largest sources of greenhouse gas emissions world-wide is the fuel used to heat (or cool) residences. Today’s review article uses advanced neural network modelling to analyse energy consumption for a large (32M population) city in southwestern China (Chongqing), using a set of 16 filtered indicators. Comparison of model results with actual ones showed less than 3% error and promise for further applications to optimize building energy consumption.
Key Quotes:
“Building energy consumption accounts for one-third of the societal energy consumption in China, and has the largest energy-saving potential”
“develops a residential building energy consumption demand model based on a back propagation (BP) neural network model”
“16 indicators of energy consumption in Chongqing residential buildings are introduced by analyzing the characteristics of Chongqing residential buildings, and then the index system of the BP neural network prediction model is established”
“There are three categories of factors effecting living energy consumption: internal factors, social factors and individual factors.. The internal factors are the main factors..which affect the living energy consumption changes…. individual factors have a great randomness and not suitable for quantitative methods…Social factors can be affected by “the five-year plan” primarily on the basis of relevant policies and regulations, so that the forecast model can be corrected, so social factors are also not considered“
“the energy value difference between the one predicted by the established BP artificial neural network model of residential building energy requirements and the actual one is quite small, with an error of 3% or less”
“The model can provide the guidelines for Chongqing residential building energy-saving programs and measures by predicting the energy consumption of residential buildings in Chongqing”
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