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

By Frédéric Magoules, Hai-Xiang Zhao

Concentrating on up to date man made intelligence types to unravel construction power difficulties, Artificial Intelligence for construction strength Analysis studies lately built types for fixing those concerns, together with specified and simplified engineering tools, statistical tools, and synthetic intelligence equipment. The textual content additionally simulates power intake profiles for unmarried and a number of structures. according to those datasets, aid Vector computing device (SVM) types are expert and confirmed to do the prediction. compatible for beginner, intermediate, and complicated readers, it is a important source for development designers, engineers, and scholars

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As an example, current programs for automatic reasoning can prove useful theorems concerning the correctness of large-scale digital circuitry, but exhibit little or no common sense. Current language-processing programs can translate simple sentences into database queries, but the programs are misled by the kind of idioms, metaphors, conversational ploys or ungrammatical Data Mining and Machine Learning in Building Energy Analysis, First Edition. Frédéric Magoulès and Hai-Xiang Zhao © ISTE Ltd 2016.

This chapter will discuss how to collect real consumption data and how to perform simulation, providing the basic datasets that will be used in later chapters for developing models. Data Mining and Machine Learning in Building Energy Analysis, First Edition. Frédéric Magoulès and Hai-Xiang Zhao © ISTE Ltd 2016. Published by ISTE Ltd and John Wiley & Sons, Inc. 2. Surveys or questionnaires Surveys or questionnaires are usually launched by utility companies, building management companies, energy analysis companies or government organizations.

The sensor used to monitor electrical energy in houses is a static (solid state) watt–hour meter, as used by many power companies. The particular meter is a single phase Siemens S2A100S meter. These meters are manufactured in large quantities and consequently the meter is cheaper than dedicated kilowatt–hour transducers. This meter also incorporates a logging capacity; however, it is limited to half-hour intervals and is capable of only storing 1-week duration of data and thus is not suitable for anything other than basic monitoring tasks.

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