By Frédéric Magoules, Hai-Xiang Zhao
Concentrating on up to date man made intelligence types to unravel construction power difficulties, Artificial Intelligence for construction power Analysis stories lately constructed types for fixing those matters, together with specific and simplified engineering tools, statistical equipment, and synthetic intelligence equipment. The textual content additionally simulates power intake profiles for unmarried and a number of constructions. in accordance with those datasets, help Vector laptop (SVM) types are educated and proven to do the prediction. compatible for beginner, intermediate, and complex readers, it is a very important source for construction designers, engineers, and scholars
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The data acquisition could be done either in-house or by use of outside consultants, depending on the level of audit required and availability of expertise and resources. Internal staff could carry out the walk-through audit. When outside help from a consultant, contractor or independent energy auditor is needed, the search and selection should be done carefully to get the 38 Data Mining and Machine Learning in Building Energy Analysis best and most reliable service. Data preprocessing can provide clean data for model development while handling uncertainties such as missing samples, outliers and noisy.
The data acquisition could be done either in-house or by use of outside consultants, depending on the level of audit required and availability of expertise and resources. Internal staff could carry out the walk-through audit. When outside help from a consultant, contractor or independent energy auditor is needed, the search and selection should be done carefully to get the 38 Data Mining and Machine Learning in Building Energy Analysis best and most reliable service. Data preprocessing can provide clean data for model development while handling uncertainties such as missing samples, outliers and noisy.
Furthermore, SVM showed the best performance among all prediction models. The models were trained on the data of 59 buildings and tested on nine buildings. Liang and Du [LIA 07] presented a cost-effective fault detection and diagnosis method for HVAC systems by combining the physical model and a SVM. By using a four-layer SVM classifier, the normal condition and three possible faults can be recognized quickly and accurately with a small number of training samples. Three major faults are recirculation damper stuck, cooling coil fouling/block and supply fan speed decreasing.