Affiliations: [a] Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX, USA | [b] Department of Architecture, Design and Urbanism, Drexel University, Philadelphia, PA, USA
Correspondence:
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Corresponding author: Cristina Nichiforov, Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX, USA. E-mail: [email protected].
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.
Keywords: Matrix profile, forecasting, LSTM, big data, load forecasting
