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Michael Wood, Emanuele Ogliari, Alfredo Nespoli, Travis Simpkins and Sonia Leva
Optimal behind-the-meter energy management often requires a day-ahead electric load forecast capable of learning non-linear and non-stationary patterns, due to the spatial disaggregation of loads and concept drift associated with time-varying physics and...
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Venkataramana Veeramsetty, Dongari Rakesh Chandra, Francesco Grimaccia and Marco Mussetta
Electrical load forecasting study is required in electric power systems for different applications with respect to the specific time horizon, such as optimal operations, grid stability, Demand Side Management (DSM) and long-term strategic planning. In th...
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Tran Thanh Ngoc, Le Van Dai, Lam Binh Minh
Pág. 258 - 269
This study investigates data standardization methods based on the grid search (GS) algorithm for energy load forecasting, including zero-mean, min-max, max, decimal, sigmoid, softmax, median, and robust, to determine the hyperparameters of deep learning ...
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Tran Thanh Ngoc, Le Van Dai, Lam Binh Minh
Pág. 258 - 269
This study investigates data standardization methods based on the grid search (GS) algorithm for energy load forecasting, including zero-mean, min-max, max, decimal, sigmoid, softmax, median, and robust, to determine the hyperparameters of deep learning ...
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Manuel Lopez-Martin, Antonio Sanchez-Esguevillas, Luis Hernandez-Callejo, Juan Ignacio Arribas and Belen Carro
This work brings together and applies a large representation of the most novel forecasting techniques, with origins and applications in other fields, to the short-term electric load forecasting problem. We present a comparison study between different cla...
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Namrye Son, Seunghak Yang and Jeongseung Na
Forecasting domestic and foreign power demand is crucial for planning the operation and expansion of facilities. Power demand patterns are very complex owing to energy market deregulation. Therefore, developing an appropriate power forecasting model for ...
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Jaehyun Lee, Jinho Kim and Woong Ko
Electric load forecasting for buildings is important as it assists building managers or system operators to plan energy usage and strategize accordingly. Recent increases in the adoption of advanced metering infrastructure (AMI) have made building electr...
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Accurate short-term electric load forecasting is significant for the smart grid. It can reduce electric power consumption and ensure the balance between power supply and demand. In this paper, the Stacked Denoising Auto-Encoder (SDAE) is adopted for shor...
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Ming-Wei Li, Jing Geng, Shumei Wang and Wei-Chiang Hong
Hybridizing evolutionary algorithms with a support vector regression (SVR) model to conduct the electric load forecasting has demonstrated the superiorities in forecasting accuracy improvements. The recently proposed bat algorithm (BA), compared with cla...
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Li-Ling Peng, Guo-Feng Fan, Min-Liang Huang and Wei-Chiang Hong
Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support v...
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Guo-Feng Fan, Shan Qing, Hua Wang, Wei-Chiang Hong and Hong-Juan Li
Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support v...
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Wei-Chiang Hong, Yucheng Dong, Chien-Yuan Lai, Li-Yueh Chen and Shih-Yung Wei
Accurate electric load forecasting has become the most important issue in energy management; however, electric load demonstrates a seasonal/cyclic tendency from economic activities or the cyclic nature of climate. The applications of the support vector r...
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Pan Duan, Kaigui Xie, Tingting Guo and Xiaogang Huang
This paper presents a new combined method for the short-term load forecasting of electric power systems based on the Fuzzy c-means (FCM) clustering, particle swarm optimization (PSO) and support vector regression (SVR) techniques. The training samples us...
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Ahmad Mohsenimanesh, Evgueniy Entchev and Filip Bosnjak
Forecasting the aggregate charging load of a fleet of electric vehicles (EVs) plays an important role in the energy management of the future power system. Therefore, accurate charging load forecasting is necessary for reliable and efficient power system ...
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Venkataramana Veeramsetty, Modem Sai Pavan Kumar and Surender Reddy Salkuti
Short-term electric power load forecasting is a critical and essential task for utilities in the electric power industry for proper energy trading, which enables the independent system operator to operate the network without any technical and economical ...
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Chaoran Zheng, Mohsen Eskandari, Ming Li and Zeyue Sun
The large-scale integration of wind power and PV cells into electric grids alleviates the problem of an energy crisis. However, this is also responsible for technical and management problems in the power grid, such as power fluctuation, scheduling diffic...
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The aim of this study is to analyze the perception of the meaning of sustainability in the food sector. A sample of 268 University students belonging to the Millennial generation was identified and a survey was carried out to assess the interaction betwe...
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Cristhian Moreno-Chaparro, Jeison Salcedo-Lagos, Edwin Rivas Trujillo, Alvaro Orjuela Canon
Pág. 94 - 106
Abstract AuthorsDownloadsReferencesHow to Cite
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Qingliang Xiong, Mingping Liu, Yuqin Li, Chaodan Zheng and Suhui Deng
Due to difficulties with electric energy storage, balancing the supply and demand of the power grid is crucial for the stable operation of power systems. Short-term load forecasting can provide an early warning of excessive power consumption for utilitie...
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Ji-Yoon Kim, Jong-Hak Lee, Ji-Hyun Oh and Jin-Seok Oh
Efficient vessel operation may reduce operational costs and increase profitability. This is in line with the direction pursued by many marine industry stakeholders such as vessel operators, regulatory authorities, and policymakers. It is also financially...
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