An intelligent approach to assessing the effect of building occupancy on building cooling load prediction

Comparisons of inverse modeling approaches for predicting building energy performance

“…Kwok [25] conducted a study using artificial neural networks to predict building cooling load. Kissock [26], Krarti et al [27] utilized neural networks to estimate energy and demand savings from retrofitting of commercial buildings.…”

Analysis and diagnosis of the energy performance of buildings and districts: Methodology, validation and development of Urban Energy Maps

“…Therefore, both heating and cooling seasons should be considered to effectively investigate and optimize the energy demand from air-conditioning. Kwok, Yuen, and Lee (2011) discuss the evaluation of cooling demand, affirming that its prediction is a key factor for permitting proper evaluation of the overall energy demand. However, while saving measures related to space heating are well-known and extensively discussed by the European legislations, prescriptions regarding reductions in cooling demand are as yet inadequate.…”

Overview of the use of artificial neural networks for energy‐related applications in the building sector

“…Thus, Paudel et al chose to use occupancy profiles and work/off day among others input parameters for heating demand prediction. Similarly, occupants dependent input parameters are used for cooling load prediction in other articles . Through those studies, it is clearly shown that occupants' behavior related parameters are preponderant for accurate prediction.…”

“…Similarly, occupants dependent input parameters are used for cooling load prediction in other articles. [82][83][84] Through those studies, it is clearly shown that occupants' behavior related parameters are preponderant for accurate prediction. Aydinalp et al 85 went further as they managed to model space heating and domestic hot water energy consumption using socioeconomic factors as part of input parameters of their neural network.…”