Modeling industrial loads in non-residential buildings

A. Vaghefi, Farbod Farzan and Mohsen A. Jafari

Applied Energy, 2015, vol. 158, issue C, 378-389

Abstract: Industrial loads in non-residential buildings have significantly contributed in total energy use throughout the world. This paper aims to develop a data-driven risk-based framework to predict and optimally control industrial loads in non-residential buildings. In the proposed framework, first, a set of predictive analytics tools are employed to identify the patterns of industrial loads over time. This also includes a high-dimensional clustering model to allocate industrial load profiles into smaller groups with less variability and same patterns. Once the patterns of industrial loads are identified, then a classification model is implemented to estimate the best class that matches with any new load profiles. Ultimately, the proposed framework provides a risk-based model to calculate and evaluate the total risk of energy decisions for the next day. This is coupled with a utility function structure to help decision makers to take best demand-side actions. The efficiency of the proposed model is investigated through a real world use case.

Keywords: Industrial load; Predictive analytics; Cost-based risk model; High-dimensional clustering; Generalized Linear Model (GLM) (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:158:y:2015:i:c:p:378-389

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DOI: 10.1016/j.apenergy.2015.08.077

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