Download Data Mining and Machine Learning in Building Energy by Frédéric Magoules, Hai-Xiang Zhao PDF

By Frédéric Magoules, Hai-Xiang Zhao

ISBN-10: 1848214227

ISBN-13: 9781848214224

Concentrating on up to date man made intelligence types to resolve construction power difficulties, Artificial Intelligence for construction strength Analysis reports lately constructed versions for fixing those matters, together with special and simplified engineering tools, statistical tools, and synthetic intelligence tools. The textual content additionally simulates power intake profiles for unmarried and a number of constructions. in accordance with those datasets, help Vector computing device (SVM) types are educated and established to do the prediction. compatible for amateur, intermediate, and complex readers, this can be a very important source for development designers, engineers, and scholars

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Additional resources for Data Mining and Machine Learning in Building Energy Analysis: Towards High Performance Computing

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4. 3. Simulation of multiple buildings In order to generate data for more buildings, we suppose that all buildings are of the same type, specifically, for office use, and have analogous characteristics. In our approach, the previously used input file is divided into two parts. The first part is called the alterable part, containing the parameters that are probably different for each building, such as structure characteristics, location, weather conditions, number of occupants, etc. Their values are obtained by stochastic methods, but should be in a reasonable domain.

Meanwhile, we take 25 variables as features. They are listed in the following category: – day type indicates if the current day is a holiday or a normal working day; – weather conditions; – zone mean air temperatures; – infiltration volume; – heat gain through each window; – heat gain through lights; – heat gain from people; – zone internal total heat gain. 4. Intuitively, the hourly consumption varies periodically. In the middle of one day, around 12 o’clock, the electricity requirement reaches maximum, while at night it is at the lowest level.

The sample includes households from large and small cities, urban and rural areas and both the North and South Islands from Kaikohe to Invercargill. Each house was monitored for about 11 months. ), and whole building energy measurement that measures the various energy flows into the building. Where possible the whole building energy measurement also includes the measurement of large energy end uses such as water heating. The experimental method for HEEP is intended to involve the measuring of energy use in at least 400 houses throughout the country.

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Data Mining and Machine Learning in Building Energy Analysis: Towards High Performance Computing by Frédéric Magoules, Hai-Xiang Zhao

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