2018
DOI: 10.1109/tii.2017.2789297
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Power Market Load Forecasting on Neural Network With Beneficial Correlated Regularization

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Cited by 37 publications
(28 citation statements)
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“…The installed power capacity of renewable energy generation grew more than 200 GW, which is mostly PV generation in 2019 [4,5]. However, because of the intermittency and uncertainty of PV, the high penetration of PV could bring great challenges to the power grid, such as power distribution system planning and operation [6][7][8][9], load demand forecasting [10][11][12], hybrid energy system configuration [13,14], and PV power forecasting [15,16].…”
Section: Background and Motivationmentioning
confidence: 99%
See 1 more Smart Citation
“…The installed power capacity of renewable energy generation grew more than 200 GW, which is mostly PV generation in 2019 [4,5]. However, because of the intermittency and uncertainty of PV, the high penetration of PV could bring great challenges to the power grid, such as power distribution system planning and operation [6][7][8][9], load demand forecasting [10][11][12], hybrid energy system configuration [13,14], and PV power forecasting [15,16].…”
Section: Background and Motivationmentioning
confidence: 99%
“…The delta solar irradiation set ∆R k beh d i , t and approximate delta PV output power set ∆P k * PV,beh d i , t can be calculated by Equations (13) and (14), according to the dates recording in Equations (11) and (12).…”
Section: Pv Output Power Sensitivity Modelmentioning
confidence: 99%
“…Artificial intelligence techniques are widely used for shortterm load forecasting, such as artificial neural network (ANN) [6], support vector machine (SVM) [7], extreme learning machine [8], Bayesian neural network [9], deep neural network [10] and recurrent neural network (RNN) [11]. In [12], long short-term memory (LSTM) is used to solve the vanishing gradient problem of RNN and outperforms other neural network methods.…”
Section: Introductionmentioning
confidence: 99%
“…On the other hand, the output power generated by RES such as wind and solar possesses difficulties in precise controlling. To address the problem of predicting model accuracy and income inconsistency, [10] proposes a useful regularization method for neural network prediction.…”
Section: Introductionmentioning
confidence: 99%