Day-ahead forecasting of solar photovoltaic output power using multilayer perceptron

Ehsan, R. Muhammad; Simon, Sishaj P.; Venkateswaran, P. R.

doi:10.1007/s00521-016-2310-z

Cited by 58 publications

(26 citation statements)

References 31 publications

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“…As for the specific day-ahead hourly forecasting PV power problem, [10] use add a least-square optimization of Numerical Weather Prediction (NWP) to a simple persistence model, to forecast solar power output for two PV plants in the American Southwest. A multilayer perceptron was used in [11] to predict the power output of a grid-connected 20-kW solar power plant in India. A stochastic ANN was adopted in combination with a deterministic Clear Sky Solar Radiation Model (CSRM) to predict the power output of four PV plants in Italy.…”

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confidence: 99%

Day-Ahead Hourly Forecasting of Power Generation From Photovoltaic Plants

Gigoni

Betti

Crisostomi

et al. 2018

IEEE Trans. Sustain. Energy

177

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The ability to accurately forecast power generation from renewable sources is nowadays recognised as a fundamental skill to improve the operation of power systems. Despite the general interest of the power community in this topic, it is not always simple to compare different forecasting methodologies, and infer the impact of single components in providing accurate predictions. In this paper we extensively compare simple forecasting methodologies with more sophisticated ones over 32 photovoltaic plants of different size and technology over a whole year. Also, we try to evaluate the impact of weather conditions and weather forecasts on the prediction of PV power generation.

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mentioning

confidence: 99%

Day-Ahead Hourly Forecasting of Power Generation From Photovoltaic Plants

Gigoni

Betti

Crisostomi

et al. 2018

IEEE Trans. Sustain. Energy

177

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“…In [16], a wavelet recurrent neural network (WRNN) was proposed for the prediction of energy production in a PV park. As for the day-ahead forecasting of hourly solar irradiance and PV power, ANN topped the methodologies for multi-input multi-output forecasting [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Day-Ahead Forecasting of Hourly Photovoltaic Power Based on Robust Multilayer Perception

et al. 2018

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Photovoltaic (PV) modules convert renewable and sustainable solar energy into electricity. However, the uncertainty of PV power production brings challenges for the grid operation. To facilitate the management and scheduling of PV power plants, forecasting is an essential technique. In this paper, a robust multilayer perception (MLP) neural network was developed for day-ahead forecasting of hourly PV power. A generic MLP is usually trained by minimizing the mean squared loss. The mean squared error is sensitive to a few particularly large errors that can lead to a poor estimator. To tackle the problem, the pseudo-Huber loss function, which combines the best properties of squared loss and absolute loss, was adopted in this paper. The effectiveness and efficiency of the proposed method was verified by benchmarking against a generic MLP network with real PV data. Numerical experiments illustrated that the proposed method performed better than the generic MLP network in terms of root mean squared error (RMSE) and mean absolute error (MAE).

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“…These data are supplied by PV power stations or numerical weather prediction (NWP). Modeling methods include artificial neural network (ANN) [7][8][9], support vector machine (SVM) [10,11] and multivariate regression [12] methods, among others. Indirect forecasting models comprise two continuous processes.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Online Forecasting Model for Ultrashort-Term Photovoltaic Power Generation

et al. 2018

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A hybrid photovoltaic (PV) forecasting model is proposed for the ultrashort-term prediction of PV output. The model contains two parts: offline modeling and online forecasting. The offline module uses historical monitoring data to establish a weather type classification model and PV output regression submodels. The online module uses real-time monitoring data for weather type identification on target days and the forecasting of irradiation intensity and temperature time series. The appropriate regression submodel can be selected based on the subsequent results, and the ultrashort-term real-time forecasting of PV output can be performed over a short time scale. The model incorporates power generation and historical meteorological data from the PV station and is suitable for practical engineering applications. In addition to the irradiation intensity and temperature, other factors related to photovoltaic output are evaluated; however, they are excluded from the model for simplicity and efficiency. The performance of the model is verified by practical modeling analysis.

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Day-ahead forecasting of solar photovoltaic output power using multilayer perceptron

Cited by 58 publications

References 31 publications

Day-Ahead Hourly Forecasting of Power Generation From Photovoltaic Plants

Day-Ahead Hourly Forecasting of Power Generation From Photovoltaic Plants

Day-Ahead Forecasting of Hourly Photovoltaic Power Based on Robust Multilayer Perception

A Hybrid Online Forecasting Model for Ultrashort-Term Photovoltaic Power Generation

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