A fuzzy nearest neighbor neural network statistical model for predicting demand for natural gas and energy cost savings in public buildings

作者：

Highlights：

• Rooftop HVAC sensors were monitored daily at various temperatures.

• Real-time decision support system forecasts MCF demand for natural gas.

• Model predicts cost savings based on price, operating expenses, cost to gas on, etc.

• Statistics, ANN, fuzzy logic, and nearest neighbor methods are used to compare demand.

摘要

•Rooftop HVAC sensors were monitored daily at various temperatures.•Real-time decision support system forecasts MCF demand for natural gas.•Model predicts cost savings based on price, operating expenses, cost to gas on, etc.•Statistics, ANN, fuzzy logic, and nearest neighbor methods are used to compare demand.

论文关键词：Energy forecasting,Artificial neural networks,Nearest neighbor method,Natural gas demand,Wireless sensor networks,Decision support system

论文评审过程：Available online 31 August 2013.

论文官网地址：https://doi.org/10.1016/j.eswa.2013.08.080