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 …

In this paper, support vector machine (SVM) is used to predict hourly building cooling load.
The hourly building cooling load prediction model based on SVM has been established, and …

Data-mining approaches for improving building cooling load predictions are presented and
analyzed on different training time scales (T-1 to T-6) in this paper. Multiple linear regression …

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 …

Since cooling and heating loads are regarded as significant parameters to examine the
energy performance of buildings, the need to predict and analyze them for the residential …

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 …

Due to the thermal inertia of building envelope and random uncertainty of occupant
behaviors, real-time and accurate forecasting for building cooling and heating loads is not …

Prediction of building energy consumption plays an important role in energy conservation,
management, and planning. Continuously improving and enhancing the performance of …

Accurate building energy consumption prediction is crucial to the rational planning of
building energy systems. The energy consumption of buildings is influenced by various …

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 …