Journal article
An intelligent big data analytics method for two-dimensional non-residential building energy forecasting
INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS, v 16(4), p691
2022
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The wide use of sensor and information technologies in buildings resulted in the massive generation of data related to its operation. Thus, there is a need for identifying patterns in data that they may use for the optimal operation of the buildings. The case of non-residential building characteristically provides a big volume of data whose analysis requires computational efficient methods. In this paper, we introduce a new big data analytic method that is applicable to forecasting energy demand in non-residential buildings. The goal is to make energy forecasts in a two-dimensional (2D) space defined by i) the electricity load and ii) gas demand. The proposed method combines the matrix profile (MP) method with a Long-Short Term Memory (LSTM) neural network. The combination of the above tools provides an efficient method in 2D hour ahead forecasting with the big data environment of smart buildings. In specific, with respect to mean average percentage error (MAPE) the combined MP-LSTM method provides a concurrent forecast of electricity and gas around 3% and 4%, respectively.
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Details
- Title
- An intelligent big data analytics method for two-dimensional non-residential building energy forecasting
- Publication Details
- INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS, v 16(4), p691
- Publisher
- IOS PRESS; AMSTERDAM
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Drexel University
- Web of Science ID
- WOS:000899297000005
- Scopus ID
- 2-s2.0-85145647346
- Other Identifier
- 991021861213304721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence