Advanced Methods for Photovoltaic Output Power Forecasting: A Review

Mellit, A.; Pavan, Alessandro; Ogliari, Emanuele; Leva, Sonia; Lughi, Vanni

doi:10.3390/app10020487

Cited by 203 publications

(110 citation statements)

References 76 publications

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“…From the Energy Management System (EMS) point of view, the 24 hours ahead forecast is needed to initially perform a strategic optimization of the system management, with the goal of, for example, minimizing the operating cost, reducing the fuel consumption over the following 24 hours and properly manage and size the battery energy storage system (BESS) [7], [8]. Approaching the operating time, additional refinements are useful to further correct the original forecast and tune the dispatching schedule in the so called intraday or very-short term forecast [9]. Eventually, during the operations, an integration of the previous forecast for the following minutes is required for the predictive power In order to address the 24 hours ahead forecast, several methodologies are adopted in literature.…”

Section: Introductionmentioning

confidence: 99%

PV Plant Power Nowcasting: A Real Case Comparative Study With an Open Access Dataset

et al. 2020

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Energy systems around the world are undergoing substantial changes, with an increasing penetration of Renewable Energy Sources. For this reason, the availability of a pool of suitable forecasting models specific for the needed time horizon and task is becoming crucial in the grid operation. In addition, nowcasting techniques aiming at provideing the power forecast for the immediate future, are more often investigated due to the spread of micro-grids and the need of facing changing electrical market environments. In this paper a novel comprehensive methodology aiming at computing the PV power forecast on different time horizons and resolutions is introduced. Moving from the 24-hours ahead prediction provided by the Physical Hybrid Artificial Neural Network (PHANN), a technique to refine the power forecast for the following 3 hours with an hourly granularity is analyzed, leveraging on newer information available during the operations. Moreover, in order to provide the power forecast for the following 30 minutes on a minutely basis, an innovative modification of a statistical technique is proposed, the robust persistence. The proposed comprehensive approach allowed to greatly reduce the overall error committed when compared with the benchmark models. Finally, the proposed methodology is validated and tested on a freely available database consisting on different parameters recorded at both the meteorological and photovoltaic test facility at SolarTech LAB , Politecnico di Milano, Milan.

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Section: Introductionmentioning

confidence: 99%

PV Plant Power Nowcasting: A Real Case Comparative Study With an Open Access Dataset

et al. 2020

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“…In the literature, there is a broad range of studies aimed at obtaining accurate forecasts. In this regard, [15]- [19] present comprehensive reviews of well-established techniques developed to forecast PV power generation. According to different factors, forecasting methods can be categorized into different groups.…”

Section: Introductionmentioning

confidence: 99%

A Day-Ahead Irradiance Forecasting Strategy for the Integration of Photovoltaic Systems in Virtual Power Plants

et al. 2020

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Encouraged by the considerable cost reduction, small-scale solar power deployment has become a reality during the last decade. However, grid integration of small-scale photovoltaic (PV) solar systems still remains unresolved. High penetration of Renewable Energy Sources (RESs) results in technical challenges for grid operators. To address this, Virtual Power Plants (VPPs) have been defined and developed to manage distributed energy resources with the aim of facilitating the integration of RESs. This paper introduces a hybrid irradiance forecasting approach aimed at facilitating the integration of PV systems into a VPP, especially when a historical irradiance dataset is exiguous or non-existent. This approach is based on Artificial Neural Networks (ANNs) and a novel similar hour-based selection algorithm, has been tested for a real PV installation, and has been validated also considering irradiance measurements from an aggregation of ground-based meteorological stations, which emulate the nodes of a VPP. Under a reduced historical dataset, the results show that the proposed similar hour-based method produces the best forecasts with regard to those obtained by the ANN-based approach. This is particularly true for one-month and two-month datasets minimizing the mean error by 16.32% and 9.07% respectively. Finally, to demonstrate the potential of the proposed approach, a comparative analysis has been carried out between the hybrid method and the most used benchmarks in the literature, namely, the persistence method and the method based on similar days. It has been demonstrated conclusively that the proposed model yields promising results regardless the length of the historical dataset.

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“…In the past years, a huge amount of literature has been published on power forecasting models for PV systems, both with regards to the prediction of the primary source (i.e., solar radiation) [20] and the prediction of the pro-duced power output [21], with attention focused recently on metaheuristic and machine learning methods [22]. Among them, Neural Networks (NNs) offer a suitable solution to the PV forecasting problem, since NN-based models show good results with energy time series for different time horizons and distributed scenarios [23].…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Networks for Multivariate Prediction of Photovoltaic Power Time Series

et al. 2020

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The large-scale penetration of renewable energy sources is forcing the transition towards the future electricity networks modeled on the smart grid paradigm, where energy clusters call for new methodologies for the dynamic energy management of distributed energy resources and foster to form partnerships and overcome integration barriers. The prediction of energy production of renewable energy sources, in particular photovoltaic plants that suffer from being highly intermittent, is a fundamental tool in the modern management of electrical grids shifting from reactive to proactive, with also the help of advanced monitoring systems, data analytics and advanced demand side management programs. The gradual move towards a smart grid environment impacts not only the operating control/management of the grid, but also the electricity market. The focus of this paper is on advanced methods for predicting photovoltaic energy output that prove, through their accuracy and robustness, to be useful tools for an efficient system management, even at prosumer's level and for improving the resilience of smart grids. Four different deep neural models for the multivariate prediction of energy time series are proposed; all of them are based on the Long Short-Term Memory network, which is a type of recurrent neural network able to deal with long-term dependencies. Additionally, two of these models also use Convolutional Neural Networks to obtain higher levels of abstraction, since they allow to combine and filter different time series considering all the available information. The proposed models are applied to real-world energy problems to assess their performance and they are compared with respect to the classic univariate approach that is used as a reference benchmark. The significance of this work is to show that, once trained, the proposed deep neural networks ensure their applicability in real online scenarios characterized by high variability of data, without requiring retraining and end-user's tricks.

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Advanced Methods for Photovoltaic Output Power Forecasting: A Review

Cited by 203 publications

References 76 publications

PV Plant Power Nowcasting: A Real Case Comparative Study With an Open Access Dataset

PV Plant Power Nowcasting: A Real Case Comparative Study With an Open Access Dataset

A Day-Ahead Irradiance Forecasting Strategy for the Integration of Photovoltaic Systems in Virtual Power Plants

Deep Neural Networks for Multivariate Prediction of Photovoltaic Power Time Series

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