A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output

Dolara, Alberto; Grimaccia, Francesco; Leva, Sonia; Mussetta, Marco; Ogliari, Emanuele

doi:10.3390/en8021138

Cited by 170 publications

(82 citation statements)

References 26 publications

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“…In this specific paper, authors employ the Physical Hybrid Artificial Neural Network (PHANN) method for the day-ahead forecast, as described in detail in [14,21]. This procedure mixes the physical Clear Sky Radiation Model (CSRM) and the stochastic ANN method as reported in Figure 2.…”

Section: Training Database Composition Approachesmentioning

confidence: 99%

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Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning

et al. 2018

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Abstract:The relevance of forecasting in renewable energy sources (RES) applications is increasing, due to their intrinsic variability. In recent years, several machine learning and hybrid techniques have been employed to perform day-ahead photovoltaic (PV) output power forecasts. In this paper, the authors present a comparison of the artificial neural network's main characteristics used in a hybrid method, focusing in particular on the training approach. In particular, the influence of different data-set composition affecting the forecast outcome have been inspected by increasing the training dataset size and by varying the training and validation shares, in order to assess the most effective training method of this machine learning approach, based on commonly used and a newly-defined performance indexes for the prediction error. The results will be validated over a one-year time range of experimentally measured data. Novel error metrics are proposed and compared with traditional ones, showing the best approach for the different cases of either a newly deployed PV plant or an already-existing PV facility.

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Section: Training Database Composition Approachesmentioning

confidence: 99%

“…Novel forecasting models were recently implemented by adding an estimate of the clear sky radiation to the series of historical local weather data, as reported in [21].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning

et al. 2018

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“…Rosato et al [22] proposed a new approach based on neural and fuzzy neural networks for PV power prediction, and the forecasting results in the training of neural networks confirm such a trend for which the performance decreases progressively as the length of the training set increases because the training process is more difficult. Dolara et al [23] proposed the hybrid method, combining an artificial intelligence technique with an analytical physical model, and the results showed that the length training set is critical to the dynamic characteristics of neural networks. Finally, the optimization for the forecasting model's parameters is attracting increasing attention.…”

Section: Introductionmentioning

confidence: 99%

An Improved Forecasting Method for Photovoltaic Power Based on Adaptive BP Neural Network with a Scrolling Time Window

Zhu

Lian

et al. 2017

Energies

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Due to the large scale of grid-connected photovoltaic (PV) power generation installations, accurate PV power forecasting is critical to the safe and economic operation of the electric power system. In the paper, by analyzing the influence of external ambient factors and the changing characteristics of PV modules with time, it is found that PV power generation is a nonlinear and time-varying process. This suggests that a certain single forecasting model is inadequate for representing actual generation characteristics, and it is difficult to obtain an accurate forecasting result. An adaptive back propagation (BP) neural network model adopting scrolling time window is proposed to solve the problem. Via an update of the training data of BP neural network with the scrolling time window, the forecasting model adapts to time and a changing external environment with the required modeling precision. Meanwhile, through evaluation of the forecasting performance in different time windows, an optimized time window can be determined to guarantee accuracy. Finally, using the actual operation data of a PV plant in Beijing, the approach is validated as being applicable for PV power forecasting and is able to effectively respond to the dynamic change of the PV power generation process. This improves the forecasting accuracy and also reduces computation complexity as compared with the conventional BP neural network algorithm.

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“…Dolara et al [43] investigated the PV power output prediction using three mathematical models and considered both poly-crystalline and mono-crystalline PV module. Dolara et al [44] and Leva et al [45] investigated the P V power output prediction using neural network and hybrid model. Some of the renowned models used for prediction, design and economic analysis of PV power plant.…”

Section: Introductionmentioning

confidence: 99%

Mathematical method to find best suited PV technology for different climatic zones of India

Chakraborty

Kumar

Haldkar

et al. 2017

Int J Energy Environ Eng

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This paper presents a reliable mathematical method to predict the energy generation from grid connected photovoltaic plant of different commercially used technologies in different zones of India. Global horizontal insolation (GHI) and daytime temperature are the two major parameters affecting the output of photovoltaic (PV) plant. Depending on those two major parameters, India is classified into 15 climatic zones. Typical Meteorological Year data were collected from National Renewable Energy Laboratory to classify India in different climatic zones. Energy generation of different commercially used PV technologies in different climatic zones of India is predicted using proposed mathematical method. These results show a decisive study to choose the best PV technology for different climatic zones of India. Results predict that in almost all climatic zones, amorphous silicon (a-Si) is the best suitable PV technology. In very low-temperature zones, irrespective of GHI, the second best suitable PV technology is mono and cadmium telluride (CdTe) as generation from these two technologies is same. Whereas in other climatic zones, after a-Si the best suitable is CdTe PV technology. Predicted energy generation is validated with the 1-year generation of 2014 from 15 working PV plants of different technologies. Predicted generation is in good co-relation with the actual real-time generation from the PV plants.

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A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output

Cited by 170 publications

References 26 publications

Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning

Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning

An Improved Forecasting Method for Photovoltaic Power Based on Adaptive BP Neural Network with a Scrolling Time Window

Mathematical method to find best suited PV technology for different climatic zones of India

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