Results 241 to 250 of about 136,820 (296)

Artificial intelligence powered intelligent energy management framework for hydrogen storage and dispatch in smart microgrids. [PDF]

Sci Rep
Hassan M.
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Intelligent demand-side energy management via optimized ANFIS-gene expression programming in hybrid renewable-grid systems. [PDF]

Sci Rep
Elboughdiri N, Kriaa K, Bakare MS, Abdulkarim A, Alaneme GU, Maatki C.   +5 more
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Optimization of unit commitment considering multiple stochastic factors and interruptible load under chance constraints. [PDF]

Sci Rep
Ding B, Zhao M, Qin X, Chen S.
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Enhancing peak demand forecasting with adaptive FastICA-transformer and entropy-based model customization. [PDF]

Sci Rep
Saeed F, Aldera S, Alkhatib M.
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Multi-scale fusion transformer for EV charging station load prediction. [PDF]

Sci Rep
Liu W, Qiao J, Wang W, Zhao X.
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Optimized energy management of PV-Powered lighting system for smart cities using perfumer optimization algorithm and graph ensemble neural network. [PDF]

Sci Rep
Hussain Z, Selvam P, Sivaramkrishnan M, Ramkumar MS, Paramasivam S, Ghouse M, Khan TMY, Khan MA, Zewdie A.   +8 more
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Detection, mining and forecasting of impact load in power load forecasting

Applied Mathematics and Computation, 2005
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wang, Jianzhou, Ma, Zhixin, Li, Lian
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Power load forecast system for Turkish electric market

2015 23nd Signal Processing and Communications Applications Conference (SIU), 2015
Forecasting the electric load demand in advance is very important in deregulated market conditions to give proper production, purchase, maintenance and investment decisions. Correct price forecasts also depend on accurate load prediction. In this study, effects of calendar, historical price and load data on short-term load forecast for Turkish ...
TAYŞİ, Ziya Cihan, BOZKURT, Ömer Özgür, BİRİCİK, Göksel   +2 more
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A Fast and Stable Forecasting Model to Forecast Power Load

International Journal of Pattern Recognition and Artificial Intelligence, 2015
As the traditional gray forecasting model GM(1, 1) has poor performance in forecasting the fast-growing power load, we present a chaotic co-evolutionary particle swarm optimization (CCPSO) algorithm, one with better efficiency than the PSO algorithm. Based on the GM(1, 1) model, the CCPSO algorithm is adopted to solve the values of parameters a and b ...
Li-Zhi Tan, A.-J. Ouyang, Xu-Yu Peng, Er-Xi Li, Tung Khac Truong, Libo Hao   +5 more
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