Yuqian Qi^*, Mingshui Li, Yu Lu, Baitong Li

Energy Engineering, Vol.120, No.5, pp. 1185-1201, 2023, DOI:10.32604/ee.2023.025574 - 20 February 2023

Abstract In view of the problem of power quality degradation of port distribution network after the large-scale application of shore power load, a method of power quality management of port distribution network is proposed. Based on the objective function of the best power quality management effect and the smallest investment cost of the management device, the optimization model of power quality management in the distribution network after the large-scale application of large-capacity shore power is constructed. Based on the balance between the economic demand of distribution network resources optimization and power quality management capability, the power More >

Jiahao Wen, Zhijian Wang^*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 749-765, 2023, DOI:10.32604/cmes.2023.023865 - 05 January 2023

Abstract Since the existing prediction methods have encountered difficulties in processing the multiple influencing factors in short-term power load forecasting, we propose a bidirectional long short-term memory (BiLSTM) neural network model based on the temporal pattern attention (TPA) mechanism. Firstly, based on the grey relational analysis, datasets similar to forecast day are obtained. Secondly, the bidirectional LSTM layer models the data of the historical load, temperature, humidity, and date-type and extracts complex relationships between data from the hidden row vectors obtained by the BiLSTM network, so that the influencing factors (with different characteristics) can select relevant… More >

Jiarui Cui^1,2,*, Qing Li^1,*, Bin Cao^2,3, Xiangquan Li¹, Qun Yan¹

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1789-1806, 2022, DOI:10.32604/cmes.2022.017958 - 30 December 2021

Abstract With the increasing demand for petroleum resources and environmental issues, new energy electric vehicles are increasingly being used. However, the large number of electric vehicles connected to the grid has brought new challenges to the operation of the grid. Firstly, A novel bidirectional interaction model is established based on modulation theory with nonlinear loads. Then, the electric energy measuring scheme of EVs for V2G is derived under the conditions of distorted power loads. The scheme is composed of fundamental electric energy, fundamental-distorted electric energy, distorted-fundamental electric energy and distorted electric energy. And the characteristics of More >

Fuyun Zhu, Guoqing Wu^*

Energy Engineering, Vol.118, No.6, pp. 1703-1712, 2021, DOI:10.32604/EE.2021.015602 - 10 September 2021

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) 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 - 16 July 2021

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 More >

Hui Li¹, Cailin Shi^{2, 3}, Xin Liu^{2, 3}, Aziguli Wulamu^{2, 3, *}, Alan Yang⁴

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 987-1004, 2020, DOI:10.32604/cmc.2020.09812 - 10 June 2020

Abstract The difference in electricity and power usage time leads to an unbalanced current among the three phases in the power grid. The three-phase unbalanced is closely related to power planning and load distribution. When the unbalance occurs, the safe operation of the electrical equipment will be seriously jeopardized. This paper proposes a Hierarchical Temporal Memory (HTM)-based three-phase unbalance prediction model consisted by the encoder for binary coding, the spatial pooler for frequency pattern learning, the temporal pooler for pattern sequence learning, and the sparse distributed representations classifier for unbalance prediction. Following the feasibility of spatialtemporal More >