Short-term Power Load Forecast of an Electrically Heated House in St. John’s, Newfoundland, Canada

Hla U May Marma, M. Tariq Iqbal and Christopher Thomas Seary
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Hla U May Marma: Memorial University of Newfoundland, Canada.
M. Tariq Iqbal: Memorial University of Newfoundland, Canada.
Christopher Thomas Seary: Memorial University of Newfoundland, Canada.

European Journal of Electrical Engineering and Computer Science, 2020, vol. 4, issue 3

Abstract: A highly efficient deep learning method for short-term power load forecasting has been developed recently. It is a challenge to improve forecasting accuracy, as power consumption data at the individual household level is erratic for variable weather conditions and random human behaviour. In this paper, a robust short-term power load forecasting method is developed based on a Bidirectional long short-term memory (Bi-LSTM) and long short-term memory (LSTM) neural network with stationary wavelet transform (SWT). The actual power load data is classified according to seasonal power usage behaviour. For each load classification, short-term power load forecasting is performed using the developed method. A set of lagged power load data vectors is generated from the historical power load data, and SWT decomposes the vectors into sub-components. A Bi-LSTM neural network layer extracts features from the sub-components, and an LSTM layer is used to forecast the power load from each extracted feature. A dropout layer with fixed probability is added after the Bi-LSTM and LSTM layers to bolster the forecasting accuracy. In order to evaluate the accuracy of the proposed model, it is compared against other developed short-term load forecasting models which are subjected to two seasonal load classifications.

Keywords: Load forecast; Stationary wavelet transform; Long short-term memory; Neural Network (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:epw:ejece0:v:4:y:2020:i:3:id:19210

DOI: 10.24018/ejece.2020.4.3.210

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