Combining forecasts of day-ahead solar power

Dewangan, Chaman Lal; Singh, S. N.; Chakrabarti, Saikat

doi:10.1016/j.energy.2020.117743

Cited by 46 publications

(17 citation statements)

References 27 publications

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“…( 16), which is employed commonly to compare the squared absolute error between the real load and the predicted load. The smaller the value of RMSE is, the closer the predicted load gets to the real load [9,56,57]. MAPE is formulated as Eq.…”

Section: Evaluation Metricsmentioning

confidence: 99%

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Multivariate empirical mode decomposition based hybrid model for day-ahead peak load forecasting

Huang¹,

Hasan

Deng³

et al. 2022

Energy

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Section: Evaluation Metricsmentioning

confidence: 99%

“…( 17), which is employed usually to evaluate the relative percentage error between the real load and the predicted load. The smaller the value of MAPE is, the better the forecasting performance of model is [9,28,57,58].…”

Section: Evaluation Metricsmentioning

confidence: 99%

Multivariate empirical mode decomposition based hybrid model for day-ahead peak load forecasting

Huang¹,

Hasan

Deng³

et al. 2022

Energy

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“…Combining individual forecasting algorithms can increase the accuracy of final forecasts for the day ahead. The frequency of retraining of the forecasting model is to be determined to optimize its performance and computational burden [15]. The temporal features of data can first be extracted by the LSTM network and combined with the spatial features determined by convolutional neural network in the hybrid model based on DL [16].…”

Section: State Of the Art In Pvp Forecastingmentioning

confidence: 99%

“…Matlab-Statistics and Machine Learning Toolbox (SMLT) for regression was used with the selected data of 26 input variables to calculate the CSI output (Figures 2 and 3) which was converted to PVP predictions at the corresponding time, analogous to D-PNN (15). SMLT comprises (Matlab-Statistics and Machine Learning toolbox for regression: https://www.…”

Section: -9 H and 24 H Csi/pvp Forecasting-data And Experimentsmentioning

confidence: 99%

Photovoltaic Energy All-Day and Intra-Day Forecasting Using Node by Node Developed Polynomial Networks Forming PDE Models Based on the L-Transformation

Zjavka

2021

Energies

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Forecasting Photovoltaic (PV) energy production, based on the last weather and power data only, can obtain acceptable prediction accuracy in short-time horizons. Numerical Weather Prediction (NWP) systems usually produce free forecasts of the local cloud amount each 6 h. These are considerably delayed by several hours and do not provide sufficient quality. A Differential Polynomial Neural Network (D-PNN) is a recent unconventional soft-computing technique that can model complex weather patterns. D-PNN expands the n-variable kth order Partial Differential Equation (PDE) into selected two-variable node PDEs of the first or second order. Their derivatives are easy to convert into the Laplace transforms and substitute using Operator Calculus (OC). D-PNN proves two-input nodes to insert their PDE components into its gradually expanded sum model. Its PDE representation allows for the variability and uncertainty of specific patterns in the surface layer. The proposed all-day single-model and intra-day several-step PV prediction schemes are compared and interpreted with differential and stochastic machine learning. The statistical models are evolved for the specific data time delay to predict the PV output in complete day sequences or specific hours. Spatial data from a larger territory and the initially recognized daily periods enable models to compute accurate predictions each day and compensate for unexpected pattern variations and different initial conditions. The optimal data samples, determined by the particular time shifts between the model inputs and output, are trained to predict the Clear Sky Index in the defined horizon.

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“…For example, in Ref. [21] day-ahead refers to a 0e24 h ahead forecast, which does not meet the operational conditions of the actual DAM [12]. In other studies it is unclear if the application of these models meet the market conditions [14,16] or it is unknown to what extent the obtained results can be achieved when adopted in real-time [15].…”

Section: Introductionmentioning

confidence: 99%

Operational Day-Ahead Solar Power Forecasting for Aggregated PV Systems with a Varying Spatial Distribution

2021

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Combining forecasts of day-ahead solar power

Cited by 46 publications

References 27 publications

Multivariate empirical mode decomposition based hybrid model for day-ahead peak load forecasting

Multivariate empirical mode decomposition based hybrid model for day-ahead peak load forecasting

Photovoltaic Energy All-Day and Intra-Day Forecasting Using Node by Node Developed Polynomial Networks Forming PDE Models Based on the L-Transformation

Operational Day-Ahead Solar Power Forecasting for Aggregated PV Systems with a Varying Spatial Distribution

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