Yunlei Zhang¹, Ruifeng Cao¹, Danhuang Dong², Sha Peng^3,*, Ruoyun Du³, Xiaomin Xu³

Energy Engineering, Vol.119, No.5, pp. 1829-1841, 2022, DOI:10.32604/ee.2022.020118

Abstract In the electricity market, fluctuations in real-time prices are unstable, and changes in short-term load are determined by many factors. By studying the timing of charging and discharging, as well as the economic benefits of energy storage in the process of participating in the power market, this paper takes energy storage scheduling as merely one factor affecting short-term power load, which affects short-term load time series along with time-of-use price, holidays, and temperature. A deep learning network is used to predict the short-term load, a convolutional neural network (CNN) is used to extract the features, and a long short-term memory… More >

K. Sukanya^*, P. Vijayakumar

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 971-982, 2023, DOI:10.32604/iasc.2023.028047

Abstract Frequency deviation has to be controlled in power generation units when there are fluctuations in system frequency. With several renewable energy sources, wind energy forecasting is majorly focused in this work which is a tough task due to its variations and uncontrollable nature. Whenever there is a mismatch between generation and demand, the frequency deviation may arise from the actual frequency 50 Hz (in India). To mitigate the frequency deviation issue, it is necessary to develop an effective technique for better frequency control in wind energy systems. In this work, heuristic Fuzzy Logic Based Controller (FLC) is developed for providing… More >

Ziad M. Ali^1,2,*, Ahmed M. Galal^1,3, Salem Alkhalaf⁴, Imran Khan⁵

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 755-767, 2023, DOI:10.32604/iasc.2023.027568

Abstract The large-scale application of renewable energy power generation technology brings new challenges to the operation of traditional power grids and energy management on the load side. Microgrid can effectively solve this problem by using its regulation and flexibility, and is considered to be an ideal platform. The traditional method of computing total transfer capability is difficult due to the central integration of wind farms. As a result, the differential evolution extreme learning machine is offered as a data mining approach for extracting operating rules for the total transfer capability of tie-lines in wind-integrated power systems. K-medoids clustering under the two-dimensional… More >

Charnon Chupong, Boonyang Plangklang^*

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5415-5427, 2022, DOI:10.32604/cmc.2022.028416

Abstract This article presents hourly load forecasting by using an incremental learning model called Online Sequential Extreme Learning Machine (OS-ELM), which can learn and adapt automatically according to new arrival input. However, the use of OS-ELM requires a sufficient amount of initial training sample data, which makes OS-ELM inoperable if sufficiently accurate sample data cannot be obtained. To solve this problem, a synthesis of the initial training sample is proposed. The synthesis of the initial sample is achieved by taking the first data received at the start of working and adding random noises to that data to create new and sufficient… More >

Muhammad Irfan¹, Ali Raza^2,*, Faisal Althobiani³, Nasir Ayub^4,5, Muhammad Idrees⁶, Zain Ali⁷, Kashif Rizwan⁴, Abdullah Saeed Alwadie¹, Saleh Mohammed Ghonaim³, Hesham Abdushkour³, Saifur Rahman¹, Omar Alshorman¹, Samar Alqhtani⁸

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4249-4265, 2022, DOI:10.32604/cmc.2022.025863

Abstract In the Smart Grid (SG) residential environment, consumers change their power consumption routine according to the price and incentives announced by the utility, which causes the prices to deviate from the initial pattern. Thereby, electricity demand and price forecasting play a significant role and can help in terms of reliability and sustainability. Due to the massive amount of data, big data analytics for forecasting becomes a hot topic in the SG domain. In this paper, the changing and non-linearity of consumer consumption pattern complex data is taken as input. To minimize the computational cost and complexity of the data, the… More >

Pudi Sekhar¹, T. J. Benedict Jose², Velmurugan Subbiah Parvathy³, E. Laxmi Lydia⁴, Seifedine Kadry⁵, Kuntha Pin⁶, Yunyoung Nam^7,*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1473-1487, 2022, DOI:10.32604/cmc.2022.022110

Abstract With the incorporation of distributed energy systems in the electric grid, transactive energy market (TEM) has become popular in balancing the demand as well as supply adaptively over the grid. The classical grid can be updated to the smart grid by the integration of Information and Communication Technology (ICT) over the grids. The TEM allows the Peer-to-Peer (P2P) energy trading in the grid that effectually connects the consumer and prosumer to trade energy among them. At the same time, there is a need to predict the load for effectual P2P energy trading and can be accomplished by the use of… 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 >

Fuyun Zhu, Guoqing Wu^*

Energy Engineering, Vol.118, No.6, pp. 1703-1712, 2021, DOI:10.32604/EE.2021.015602

Abstract Accurate power load forecasting plays an important role in the power dispatching and security of grid. In this paper, a mathematical model for power load forecasting based on the random forest regression (RFR) was established. The input parameters of RFR model were determined by means of the grid search algorithm. The prediction results for this model were compared with those for several other common machine learning methods. It was found that the coefficient of determination (R²) of test set based on the RFR model was the highest, reaching 0.514 while the corresponding mean absolute error (MAE) and the mean squared… More >

Lijo Jacob Varghese¹, K. Dhayalini², Suma Sira Jacob³, Ihsan Ali^4,*, Abdelzahir Abdelmaboud⁵, Taiseer Abdalla Elfadil Eisa⁶

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1053-1067, 2022, DOI:10.32604/cmc.2022.019435

Abstract Peer-to-Peer (P2P) electricity trading is a significant research area that offers maximum fulfilment for both prosumer and consumer. It also decreases the quantity of line loss incurred in Smart Grid (SG). But, uncertainities in demand and supply of the electricity might lead to instability in P2P market for both prosumer and consumer. In recent times, numerous Machine Learning (ML)-enabled load predictive techniques have been developed, while most of the existing studies did not consider its implicit features, optimal parameter selection, and prediction stability. In order to overcome fulfill this research gap, the current research paper presents a new Multi-Objective Grasshopper… More >

Bingbing Chen^*, Zhengyi Zhu, Xuyan Wang, Can Zhang

Energy Engineering, Vol.118, No.5, pp. 1499-1514, 2021, DOI:10.32604/EE.2021.015145

Abstract To solve the medium and long term power load forecasting problem, the combination forecasting method is further expanded and a weighted combination forecasting model for power load is put forward. This model is divided into two stages which are forecasting model selection and weighted combination forecasting. Based on Markov chain conversion and cloud model, the forecasting model selection is implanted and several outstanding models are selected for the combination forecasting. For the weighted combination forecasting, a fuzzy scale joint evaluation method is proposed to determine the weight of selected forecasting model. The percentage error and mean absolute percentage error of… More >