Zhiqiu Yu^1,*, Shuo-Yan Chou¹, Phan Nguyen Ky Phuc², Tiffany Hui-Kuang Yu³

Computer Systems Science and Engineering, Vol.41, No.3, pp. 1191-1205, 2022, DOI:10.32604/csse.2022.021239

Abstract This research aims to study the sustainability of Taiwan power supply chain based on system dynamics forecasting. The paper tries to investigate electricity shortage effects not only on the industrial side, but also from the standpoint of society. In our model, different forecasting methods such as linear regression, time series analysis, and gray forecasting are also considered to predict the parameters. Further tests such as the structure, dimension, historical fit, and sensitivity of the model are also conducted in this paper. Through analysis forecasting result, we believe that the demand for electricity in Taiwan will continue to increase to a… More >

Saman Taheri¹, Behnam Talebjedi^2,*, Timo Laukkanen²

Energy Engineering, Vol.118, No.6, pp. 1577-1594, 2021, DOI:10.32604/EE.2021.017795

Abstract Load forecasting is critical for a variety of applications in modern energy systems. Nonetheless, forecasting is a difficult task because electricity load profiles are tied with uncertain, non-linear, and non-stationary signals. To address these issues, long short-term memory (LSTM), a machine learning algorithm capable of learning temporal dependencies, has been extensively integrated into load forecasting in recent years. To further increase the effectiveness of using LSTM for demand forecasting, this paper proposes a hybrid prediction model that incorporates LSTM with empirical mode decomposition (EMD). EMD algorithm breaks down a load time-series data into several sub-series called intrinsic mode functions (IMFs).… More >

Vin Cent Tai^1,*, Yong Chai Tan¹, Nor Faiza Abd Rahman¹, Hui Xin Che², Chee Ming Chia², Lip Huat Saw³, Mohd Fozi Ali⁴

Energy Engineering, Vol.118, No.3, pp. 715-725, 2021, DOI:10.32604/EE.2021.014865

Abstract Electricity demand is also known as load in electric power system. This article presents a Long-Term Load Forecasting (LTLF) approach for Malaysia. An Artificial Neural Network (ANN) of 5-layer Multi-Layered Perceptron (MLP) structure has been designed and tested for this purpose. Uncertainties of input variables and ANN model were introduced to obtain the prediction for years 2022 to 2030. Pearson correlation was used to examine the input variables for model construction. The analysis indicates that Primary Energy Supply (PES), population, Gross Domestic Product (GDP) and temperature are strongly correlated. The forecast results by the proposed method (henceforth referred to as… More >