Recurrent Neural Network-Based Hourly Prediction of Photovoltaic Power Output Using Meteorological Information

Lee, Donghun; Kim, Kwanho

doi:10.3390/en12020215

Cited by 126 publications

(84 citation statements)

References 57 publications

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“…However, in general, the weak point of hybrid forecast techniques is that they underperform when meteorological conditions are unstable [47]. Here [48] different Neural Network PV power output methods have been compared with long-and short-term memory (LSTM)-based models, which seem capable of recording the hidden relationships between weather parameters and actual PV power outputs from hourly patterns to seasonal patterns across days.…”

Section: Introductionmentioning

confidence: 99%

Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques

et al. 2019

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We compare the 24-hour ahead forecasting performance of two methods commonly used for the prediction of the power output of photovoltaic systems. Both methods are based on Artificial Neural Networks (ANN), which have been trained on the same dataset, thus enabling a much-needed homogeneous comparison currently lacking in the available literature. The dataset consists of an hourly series of simultaneous climatic and PV system parameters covering an entire year, and has been clustered to distinguish sunny from cloudy days and separately train the ANN. One forecasting method feeds only on the available dataset, while the other is a hybrid method as it relies upon the daily weather forecast. For sunny days, the first method shows a very good and stable prediction performance, with an almost constant Normalized Mean Absolute Error, NMAE%, in all cases (1% < NMAE% < 2%); the hybrid method shows an even better performance (NMAE% < 1%) for two of the days considered in this analysis, but overall a less stable performance (NMAE% > 2% and up to 5.3% for all the other cases). For cloudy days, the forecasting performance of both methods typically drops; the performance is rather stable for the method that does not use weather forecasts, while for the hybrid method it varies significantly for the days considered in the analysis.Energies 2019, 12, 1621 2 of 15 measurements and data from weather forecast providers [7] are needed in order to optimize the operation of these systems. The benefits of such models are manifold. Considering the point of view of the owners of residential and Commercial-Industrial (C&I) PV plants, and specifically the possibility of operating in a grid-parity regime or better, accurate forecasts enable the maximization of the self-consumed energy and minimization of the Levelized Cost of the Energy produced (LCoE) [8,9]. Also, when residential and C&I systems operate as part of a microgrid, the power forecasts can be used as input for the Energy Management System (EMS) used to optimize the State of Charge (SoC) of the storage devices [10]. On the other hand, considering larger-scale plants and fuel parity [11], the managers of utility-scale PV plants use the forecasts to optimally plan the downtime of plants for maintenance purposes. Moreover, in countries with a day-ahead electricity market, forecast models allow for optimization of the schedule for the supply offers on the market, avoiding penalties and reduced revenues [12,13]. On the other hand, Distribution System Operators (DSOs) and Transmission System Operators (TSOs) also need reliable forecasts in order to handle the uncertainty and fluctuations of the PV distributed generators connected to their grids. The availability of reliable forecasts allows DSOs and TSOs to cope with the intermittent nature of PV plants, avoiding problems in balancing power generation and load demand [14], enhancing the stability of the system, and reducing the cost of ancillary services [15,16]. Accurate forecasts enhance reliability, and reduce costs by allowing effi...

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Section: Introductionmentioning

confidence: 99%

Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques

et al. 2019

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“…As the arrival of the era of big data, deep learning has been greatly developed. Scholars have proposed several time-series prediction methods based on in-depth learning, including Long Short-Term Memory (LSTM) [32,33], the Recurrent Neural Network (RNN) [34,35], and other prediction methods based on deep learning [36,37], which are used to forecast photovoltaic power generation, energy demand, and power load [36,[38][39][40][41]. Liu et al (2016) established the Gated Recurrent Unit prediction model (GRU) and forecasted China's primary energy demand [40].…”

Section: Soft-computing Technologymentioning

confidence: 99%

“…Liu et al (2016) established the Gated Recurrent Unit prediction model (GRU) and forecasted China's primary energy demand [40]. Lee and Kim (2019) used ANN, DNN, and LSTM to forecast photovoltaic power combined with meteorological information [41]. However, ANN and time-series prediction models based on deep learning have better performance in the training of large samples.…”

Section: Soft-computing Technologymentioning

confidence: 99%

A Novel Adaptive Intelligent Ensemble Model for Forecasting Primary Energy Demand

Zhao

Yao

et al. 2019

Energies

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Effectively forecasting energy demand and energy structure helps energy planning departments formulate energy development plans and react to the opportunities and challenges in changing energy demands. In view of the fact that the rolling grey model (RGM) can weaken the randomness of small samples and better present their characteristics, as well as support vector regression (SVR) having good generalization, we propose an ensemble model based on RGM and SVR. Then, the inertia weight of particle swarm optimization (PSO) is adjusted to improve the global search ability of PSO, and the improved PSO algorithm (APSO) is used to assign the adaptive weight to the ensemble model. Finally, in order to solve the problem of accurately predicting the time-series of primary energy consumption, an adaptive inertial weight ensemble model (APSO-RGM-SVR) based on RGM and SVR is constructed. The proposed model can show higher prediction accuracy and better generalization in theory. Experimental results also revealed outperformance of APSO-RGM-SVR compared to single models and unoptimized ensemble models by about 85% and 32%, respectively. In addition, this paper used this new model to forecast China’s primary energy demand and energy structure.

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“…Several efforts are being made to improve power prediction and, therefore, guarantee energy security and increase potential revenues of renewable systems. PV power output prediction models based on artificial learning were developed e.g., by [2] and [3], in order to learn the underlying relationships between meteorological information and actual power outputs. The relationship between sky appearance and the future PV power output using deep learning has been proposed by [4].…”

Section: Introductionmentioning

confidence: 99%

Artificial Learning Dispatch Planning with Probabilistic Forecasts: Using Uncertainties as an Asset

2020

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Weather forecast uncertainty is a key element for energy market volatility. By intelligently considering uncertainties on the schedule development, renewable energy systems with storage could improve dispatching accuracy, and therefore, effectively participate in electricity wholesale markets. Deterministic forecasts have been traditionally used to support dispatch planning, representing reduced or no uncertainty information about the future weather. Aiming at better representing the uncertainties involved, probabilistic forecasts have been developed to increase forecasting accuracy. For the dispatch planning, this can highly influence the development of a more precise schedule. This work extends a dispatch planning method to the use of probabilistic weather forecasts. The underlying method used a schedule optimizer coupled to a post-processing machine learning algorithm. This machine learning algorithm was adapted to include probabilistic forecasts, considering their additional information on uncertainties. This post-processing applied a calibration of the planned schedule considering the knowledge about uncertainties obtained from similar past situations. Simulations performed with a concentrated solar power plant model following the proposed strategy demonstrated promising financial improvement and relevant potential in dealing with uncertainties. Results especially show that information included in probabilistic forecasts can increase financial revenues up to 15% (in comparison to a persistence solar driven approach) if processed in a suitable way.

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Recurrent Neural Network-Based Hourly Prediction of Photovoltaic Power Output Using Meteorological Information

Cited by 126 publications

References 57 publications

Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques

Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques

A Novel Adaptive Intelligent Ensemble Model for Forecasting Primary Energy Demand

Artificial Learning Dispatch Planning with Probabilistic Forecasts: Using Uncertainties as an Asset

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