Extensive comparison of physical models for photovoltaic power forecasting

Mayer, Martin János; Gróf, Gyula

doi:10.1016/j.apenergy.2020.116239

Cited by 215 publications

(67 citation statements)

References 85 publications

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“…Moreover, with respect to the number of parameters, model ( 3) is parsimonious and linear. The model in (3) gives excellent accuracy when fitted to actual measured data due to its simplicity.…”

Section: Modeling Of Grey Box Based Neural Network Athe Pvusa Modelmentioning

confidence: 99%

“…PV power prediction techniques are classified into statistical and physical methods depending upon the prediction models [2]. The physical methods [3], [4] use the meteorological variable (pressure, temperature, solar irradiance, humidity, etc.) at the PV module for power prediction, whereas; statistical techniques use historical data of PV systems to predict the output power.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Prediction Error-Based Power Forecasting Scheme for Real PV System Using PVUSA Model: A Grey Box-Based Neural Network Approach

et al. 2021

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This paper presents a prediction error-based power forecasting (PEBF) method for a Photovoltaic (PV) system, using Photovoltaics for Utility Scale Applications (PVUSA) model based grey box neural network (GBNN). First, the differential equation based PVUSA model is transformed into a neural network. In the proposed PEBF scheme, the neural network is set to train whenever the difference between predicted and output powers increases from a certain threshold defined based on system dynamics and requirements. The unique design of the PVUSA model based grey box neural network takes far less training time than usual black-box neural network based models. This gives the proposed prediction scheme an advantage of updating the prediction model parameters from frequent training of neural networks with the change in metrological variables. The effectiveness of the proposed prediction scheme is demonstrated by a real case study regarding a 20MW grid-connected PV system located in Dongying city of Shandong province China. To evaluate the efficiency of the developed scheme, different assessment metrics, mean absolute error (MAE), root mean square error (RMSE), weighted mean absolute error (WMAE) and coefficient of determination ( 2 ) are applied. The average values of MAE, RMSE, and WMAE were 0.12 %, 0.20% and 0.23% respectively for all cases. The results demonstrate that the proposed scheme predicts the PV power efficiently within the defined error tolerance level, which shows the effectiveness and feasibility of the proposed prediction scheme. The prediction accuracy of the proposed scheme has been compared with the conventional black box neural network models and reveals outperformed performance with respect to prediction accuracy improvement. The proposed prediction scheme will help to balance power production and demands across integrated networks through economic dispatch decisions between the power sources.INDEX TERMS Solar photovoltaic system, grey box neural network, prediction error based power forecasting scheme, real case study, renewable energy.

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Section: Modeling Of Grey Box Based Neural Network Athe Pvusa Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Prediction Error-Based Power Forecasting Scheme for Real PV System Using PVUSA Model: A Grey Box-Based Neural Network Approach

et al. 2021

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“…The Persistence Skill Score (PSS), commonly known as the persistent forecasting model, assumes that the values of solar irradiation and wind speed in the next period are the same as the previous one. Equation (7) describes the calculation of future value, where y is the measurement of the variable [23,24].…”

Section: Persistence Skill Score (Pss)mentioning

confidence: 99%

Optimal Management of a Microgrid with Radiation and Wind-Speed Forecasting: A Case Study Applied to a Bioclimatic Building

et al. 2021

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An Energy Management System (EMS) that uses a Model Predictive Control (MPC) to manage the flow of the microgrids is described in this work. The EMS integrates both wind speed and solar radiation predictors by using a time series to perform the primary grid forecasts. At each sampling data measurement, the power of the photovoltaic system and wind turbine are predicted. Then, the MPC algorithm uses those predictions to obtain the optimal power flows of the microgrid elements and the main network. In this work, three time-series predictors are analyzed. As the results will show, the MPC strategy becomes a powerful energy management tool when it is integrated with the Double Exponential Smoothing (DES) predictor. This new scheme of integrating the DES method with an MPC presents a good management response in real-time and overcomes the results provided by the Optimal Power Flow method, which was previously proposed in the literature. For the case studies, the test microgrid located in the CIESOL bioclimatic building of the University of Almeria (Spain) is used.

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“…The I-V and P-V characteristics generated from the simulations were compared with values obtained from the experiment described in Section 3.1. The relative error, RE for the each reading is calculated using the formula presented in Equation ( 7) [62] where t s and t exp are the simulation and experimental values respectively. Tables A1-A4 in the Appendix demonstrated the comparison of simulation and experimental values (short circuit current, open circuit voltage, maximum power, fill factor, opto-electronic gain and the relative error) for the RADTIRC-PV structure and the bare PV cell.…”

Section: Simulation Processmentioning

confidence: 99%

Mathematical Modelling of a Static Concentrating Photovoltaic: Simulation and Experimental Validation

et al. 2021

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For the past twenty years, there has been increasing interest and investment in solar photovoltaic (PV) technology. One particular area of interest is the development of concentrating PV (CPV), especially for use in building integration. Many CPV designs have been developed and investigated. This paper aims at producing a mathematical modelling using MATLAB programme to predict the current-voltage (I-V) and power-voltage (P-V) characteristics of a static CPV. The MATLAB programme could also simulate the angular response of the CPV designs-which has never been explored in the previous literature. In this paper, a CPV known as the rotationally asymmetrical dielectric totally internally reflecting concentrator (RADTIRC) was analysed. A specific RADTIRC design that has an acceptance angle of ±40° was investigated in this paper. A mathematical modelling was used to simulate the angular characteristics of the RADTIRC from −50° to 50° with an increment 5°. For any CPV, we propose that the value of opto-electronic gain, Copto-e needs to be included in the mathematical model, which were obtained from experiments. The range of incident angle (±50°) was selected to demonstrate that the RADTIRC is capable of capturing the sun rays within its acceptance angle of ±40°. In each simulation, the I-V and P-V characteristics were produced, and the short circuit current (Isc), the open-circuit voltage (Voc), the maximum power (Pmax), the fill factor (FF) and the opto-electronic gain (Copto-e) were determined and recorded. The results from the simulations were validated via experiments. It was found that the simulation model is able to predict the I-V and P-V characteristics of the RADTIRC as well as its angular response, with the highest error recorded for the Isc, Voc, Pmax, FF and Copto-e was 2.1229%, 5.3913%, 9.9681%, 4.4231% and 0.0000% respectively when compared with the experiment.

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Extensive comparison of physical models for photovoltaic power forecasting

Cited by 215 publications

References 85 publications

A Novel Prediction Error-Based Power Forecasting Scheme for Real PV System Using PVUSA Model: A Grey Box-Based Neural Network Approach

A Novel Prediction Error-Based Power Forecasting Scheme for Real PV System Using PVUSA Model: A Grey Box-Based Neural Network Approach

Optimal Management of a Microgrid with Radiation and Wind-Speed Forecasting: A Case Study Applied to a Bioclimatic Building

Mathematical Modelling of a Static Concentrating Photovoltaic: Simulation and Experimental Validation

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