Modeling of a simplified hybrid algorithm for short-term load forecasting in a power system network
ISSN: 0332-1649
Article publication date: 15 July 2021
Issue publication date: 20 August 2021
Abstract
Purpose
The purpose of this paper is to develop a hybrid algorithm, which is a blend of auto-regressive integral moving average (ARIMA) and multilayer perceptron (MLP) for addressing the non-linearity of the load time series.
Design/methodology/approach
Short-term load forecasting is a complex process as the nature of the load-time series data is highly nonlinear. So, only ARIMA-based load forecasting will not provide accurate results. Hence, ARIMA is combined with MLP, a deep learning approach that models the resultant data from ARIMA and processes them further for Modelling the non-linearity.
Findings
The proposed hybrid approach detects the residuals of the ARIMA, a linear statistical technique and models these residuals with MLP neural network. As the non-linearity of the load time series is approximated in this error modeling process, the proposed approach produces accurate forecasting results of the hourly loads.
Originality/value
The effectiveness of the proposed approach is tested in the laboratory with the real load data of a metropolitan city from South India. The performance of the proposed hybrid approach is compared with the conventional methods based on the metrics such as mean absolute percentage error and root mean square error. The comparative results show that the proposed prediction strategy outperforms the other hybrid methods in terms of accuracy.
Keywords
Citation
Mayilsamy, K., A,, M.A.J., Akbarali, M.S. and Sathiyanarayanan, H. (2021), "Modeling of a simplified hybrid algorithm for short-term load forecasting in a power system network", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 40 No. 3, pp. 676-688. https://doi.org/10.1108/COMPEL-01-2021-0005
Publisher
:Emerald Publishing Limited
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