Short-term electric load forecasting for buildings using logistic mixture vector autoregressive model with curve registration

Dongyeon Jeong, Chiwoo Park and Young Myoung Ko

Applied Energy, 2021, vol. 282, issue PB, No S0306261920316408

Abstract: This paper proposes a day-ahead electric load forecasting model for buildings where daily load curves follow a few distinctive patterns. A pattern lasts for several days before changing into another. We particularly explore the problem that the day-ahead curve mostly depends on the load pattern history and is relatively insensitive to external environments such as weather conditions. The problem considers clusters for daily curve patterns and a day-ahead electric curve forecast from previous electric load and pattern history. We propose a model called the logistic mixture vector autoregressive model (LMVAR) that combines both clustering and forecasting in a single model through the expectation–maximization (EM) algorithm. To improve internal clustering performance, we apply the curve registration technique to the model. We test two models (the models with/without curve registration) with electric load data sets collected from a library and a grocery store. We then compare them with existing forecasting methods such as persistence, sequence-to-sequence long short-term memory (S2S LSTM), seasonal autoregressive (SAR), multiple-output support vector machine (M-SVM), multilayer perceptron (MLP), and a cluster-based model. The result shows that the proposed models outperform the benchmark methods.

Keywords: Electric load forecasting for buildings; Time series data; Curve registration; Logistic mixture; Vector autoregressive; Principal component analysis (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (14)

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DOI: 10.1016/j.apenergy.2020.116249

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