Hybrid PV Power Forecasting Methods: A Comparison of Different Approaches

Niccolai, Alessandro; Dolara, Alberto; Ogliari, Emanuele

doi:10.3390/en14020451

Cited by 39 publications

(16 citation statements)

References 33 publications

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“…The article by Niccolai et al [40] analyzes the forecast accuracy of three hybrid models that integrate physical elements of the system with ANNs. The first model combines ANNs with the output of the five-parameter physical model of a photovoltaic module where the parameters are obtained from a data file.…”

Section: Related Workmentioning

confidence: 99%

“…Although there are works that combine RNN techniques with statistical methods, such as ARMA, ARIMA, SAR-IMA, and Pearson's coefficients, among others ( [22,26,35]), in most cases, they do not use a large volume of data for model training and validation. In addition, few works were found that address this type of prediction through hybrid methods ( [34,40]).…”

Section: Related Workmentioning

confidence: 99%

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Daily Prediction Model of Photovoltaic Power Generation Using a Hybrid Architecture of Recurrent Neural Networks and Shallow Neural Networks

Castillo-Rojas

Bekios-Calfa

Hernández

2023

International Journal of Photoenergy

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In recent years, photovoltaic energy has become one of the most implemented electricity generation options to help reduce environmental pollution suffered by the planet. Accuracy in this photovoltaic energy forecasting is essential to increase the amount of renewable energy that can be introduced to existing electrical grid systems. The objective of this work is based on developing various computational models capable of making short-term forecasting about the generation of photovoltaic energy that is generated in a solar plant. For the implementation of these models, a hybrid architecture based on recurrent neural networks (RNN) with long short-term memory (LSTM) or gated recurrent units (GRU) structure, combined with shallow artificial neural networks (ANN) with multilayer perceptron (MLP) structure, is established. RNN models have a particular configuration that makes them efficient for processing ordered data in time series. The results of this work have been obtained through controlled experiments with different configurations of its hyperparameters for hybrid RNN-ANN models. From these, the three models with the best performance are selected, and after a comparative analysis between them, the forecasting of photovoltaic energy production for the next few hours can be determined with a determination coefficient of 0.97 and root mean square error (RMSE) of 0.17. It is concluded that the proposed and implemented models are functional and capable of predicting with a high level of accuracy the photovoltaic energy production of the solar plant, based on historical data on photovoltaic energy production.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Daily Prediction Model of Photovoltaic Power Generation Using a Hybrid Architecture of Recurrent Neural Networks and Shallow Neural Networks

Castillo-Rojas

Bekios-Calfa

Hernández

2023

International Journal of Photoenergy

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“…Due to their intrinsic stochastic nature, RES can introduce instability in the electric network. Thus, a key element for obtaining a feasible energy transition is the capability to achieve a good forecasting of RES production and electric load [8]. Many works in the literature address the problem of either short-term [10] or long-term forecasting [9].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Artificial Intelligence Energy Management System for Urban Building Integrating Photovoltaic and Storage

et al. 2023

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The emerging leading role of green energy in our society pushes the investigation of new economic and technological solutions. Green energies and smart communities increase efficiency with the use of digital solutions for the benefits of inhabitants and companies. The paper focuses on the development of a methodology for the energy management, combining photovoltaics and storage systems, considering as the main case study a multi-story building characterized by a high density of households, used to generate data which allow feasibility foresights. The physical model of the algorithm is composed by two main elements: the photovoltaics modules and the battery energy storage system. In addition, to gain information about the real-time consumption a machine learning module is included in our approach to generate predictions about the near future demand. The benefits provided by the method are evaluated with an economic analysis, which computes the return of the investment using the real consumptions of a Boarding School, located in Turin (Italy). The case study analyzed in this article showed an increase in purchased energy at the minimum price from 25% to 91% and a 55% reduction in the electricity bill compared to most solutions on the market, with no additional costs and a stabilizing effect on the grid. Finally, the economic analysis shows that the proposed method is a profitable investment, with a breakeven point of thirteen years, due to the very simple implementation and the zero additional cost requested.INDEX TERMS Deep learning, energy management systems, energy storage, environmental economics, renewable energy sources. FRANCESCO GRIMACCIA (Senior Member, IEEE) received the M.S. and Ph.D. degrees (cum laude) in electrical engineering from the Politecnico di Milano, Milan, Italy, in 2003 and 2007, respectively. He is currently an Associate Professor with the Energy Department, Politecnico di Milano. His main research interest includes softcomputing technique development and applications in different electric engineering fields, such as unmanned systems, photovoltaics, and other energy-harvesting devices. He is a member of the IEEE Computational Intelligence Society and the Vice-President of the Associazione Italiana di Elettrotecnica, Elettronica and the Automazione, Informatica e Telecomunicazioni, Milan Section.

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“…In the last decade, the recent developments in machine learning on the one hand and developments in smart meters to provide real data on the other hand make machine learning techniques quite popular for time series point forecast. Therefore, many researchers have focused on point forecast of PV generation 4,10‐12 . The point forecast provides the expected PV generation for each time step over the time horizon.…”

Section: Introductionmentioning

confidence: 99%

Data‐driven forecasting of localPVgeneration for stochasticPV‐battery system management

Kaffash

Bruninx

2021

Int J Energy Res

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Summary Power systems face more uncertainty by increasing photovoltaic system installations on the roof of buildings. To optimally manage energy and available flexibility in a building, stochastic optimization is used to take an optimal decision under uncertainty and minimize the operational cost. In stochastic optimization, a scenario set is used as an input to represent the uncertainty in a random variable, PV energy generation in this case. In this paper, a data‐driven method is proposed to obtain the distribution of the random variable and later generate scenario sets representing the uncertainty on day‐ahead PV energy generation installed on the roof of a building. This method is only based on historical PV generation and it does not require any other external data such as weather forecasts. A machine learning‐based technique is applied to forecast the PV energy production following by generating a scenario set for day‐ahead decision‐making. Later, the day‐ahead PV‐battery system management problem is formulated as a two‐stage stochastic optimization while the generated scenario set is the input of this optimization. The proposed algorithm is tested in the day‐ahead scheduling of a PV‐battery system for a commercial building, informed by real‐life measurement data. The results show that the proposed algorithm is able to capture the uncertainty in PV system while providing a cost‐optimal and reliable solution to the application problem. Without using any weather data, the error of the proposed PV energy forecast method reaches to NRMSE = 11.89, while there is 5% reduction in the operational cost of the proposed two‐stage stochastic optimization. Moreover, the proposed algorithm is easy to implement in the energy management system of a building to manage PV‐battery system.

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Hybrid PV Power Forecasting Methods: A Comparison of Different Approaches

Cited by 39 publications

References 33 publications

Daily Prediction Model of Photovoltaic Power Generation Using a Hybrid Architecture of Recurrent Neural Networks and Shallow Neural Networks

Daily Prediction Model of Photovoltaic Power Generation Using a Hybrid Architecture of Recurrent Neural Networks and Shallow Neural Networks

An Efficient Artificial Intelligence Energy Management System for Urban Building Integrating Photovoltaic and Storage

Data‐driven forecasting of localPVgeneration for stochasticPV‐battery system management

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