Modeling of a simplified hybrid algorithm for short-term load forecasting in a power system network

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.

A fractional-order equivalent model for characterizing the interelectrode capacitance of MOSFETs

“…However, it has been widely confirmed that, the performances of these components are prone to be affected by parasitic parameters such as inter-electrode capacitance and resistance (Allagui et al , 2021), and prone to be affected by parameter drift phenomenon, which is in direct contact with some dynamic characteristics of MOSFETs (Chen et al , 2020; Mukunoki et al , 2018a, 2018b), Miller plateau and oscillations at the turn on/off moments, to name a few. Therefore, a reasonable and accurate model has a pivotal role in the performance analysis of both MOSFETs and circuit systems with such components (Palanichamy and Balamurugan, 2014; Mayilsamy et al , 2021; Hsu et al , 2020).…”