General regression neural networks (GRNN) were designed and trained to investigate the
feasibility of using this technology to optimize HVAC thermal energy storage in public …

This study presents a methodology to forecast diurnal cooling load energy consumption for
institutional buildings using data driven techniques. The cases for three institutional …

To operate energy supply plants properly from the viewpoints of stable energy supply, and
energy and cost savings, it is important to predict energy demands accurately as basic …

This study presents artificial neural network (ANN) methods in building energy use
predictions. Applications of the ANN methods in energy audits and energy savings …

This study presents four modeling techniques for the prediction of hourly cooling load in the
building. In addition to the traditional back propagation neural network (BPNN), the radial …

Predicting building energy consumption is essential for planning and managing energy
systems. In recent times, numerous studies focus on load forecasting models dealing with a …

Building cooling load prediction is one of the key factors in the success of energy-saving
measures. Many computational models available in the industry today have been developed …

Building cooling load prediction is one of the key factors in the success of energy-saving
measures. Many computational models available in the industry have been developed from …

The present research work concerns development of regression models to predict the
monthly heating demand for single-family residential sector in temperate climates, with the …

General regression neural networks (GRNNs) were used to optimize air conditioning
setback scheduling in public buildings. To save energy, the temperature inside these …