Deep Learning-Based Short-Term Load Forecasting for Supporting Demand Response Program in Hybrid Energy System

Sholeh Hadi Pramono, Mahdin Rohmatillah, Eka Maulana, Rini Nur Hasanah and Fakhriy Hario
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Sholeh Hadi Pramono: Department of Elctrical Engineering, University of Brawijaya, Veteran Road, Lowokwaru, Malang 65113, Indonesia
Mahdin Rohmatillah: Department of Elctrical Engineering, University of Brawijaya, Veteran Road, Lowokwaru, Malang 65113, Indonesia
Eka Maulana: Department of Elctrical Engineering, University of Brawijaya, Veteran Road, Lowokwaru, Malang 65113, Indonesia
Rini Nur Hasanah: Department of Elctrical Engineering, University of Brawijaya, Veteran Road, Lowokwaru, Malang 65113, Indonesia
Fakhriy Hario: Department of Elctrical Engineering, University of Brawijaya, Veteran Road, Lowokwaru, Malang 65113, Indonesia

Energies, 2019, vol. 12, issue 17, 1-16

Abstract: A novel method for short-term load forecasting (STLF) is proposed in this paper. The method utilizes both long and short data sequences which are fed to a wavenet based model that employs dilated causal residual convolutional neural network (CNN) and long short-term memory (LSTM) layer respectively to hourly forecast future load demand. This model is aimed to support the demand response program in hybrid energy systems, especially systems using renewable and fossil sources. In order to prove the generality of our model, two different datasets are used which are the ENTSO-E (European Network of Transmission System Operators for Electricity) dataset and ISO-NE (Independent System Operator New England) dataset. Moreover, two different ways of model testing are conducted. The first is testing with the dataset having identical distribution with validation data, while the second is testing with data having unknown distribution. The result shows that our proposed model outperforms other deep learning-based model in terms of root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). In detail, our model achieves RMSE, MAE, and MAPE equal to 203.23, 142.23, and 2.02 for the ENTSO-E testing dataset 1 and 292.07, 196.95 and 3.1 for ENTSO-E dataset 2. Meanwhile, in the ISO-NE dataset, the RMSE, MAE, and MAPE equal to 85.12, 58.96, and 0.4 for ISO-NE testing dataset 1 and 85.31, 62.23, and 0.46 for ISO-NE dataset 2.

Keywords: short-term load forecasting; deep learning; wavenet; long short-term memory; demand response; hybrid energy system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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