Day-Ahead Probabilistic Photovoltaic Power Forecasting Models Based on Quantile Regression Neural Networks

Fernández-Jiménez, L. Alfredo; Terreros-Olarte, Sonia; Mendoza-Villena, Montserrat; Garcia-Garrido, Eduardo; Zorzano-Alba, Enrique; Lara-Santillan, Pedro M.; Zorzano-Santamaría, Pedro J.; Falces, Alberto

doi:10.1109/eecs.2017.60

Cited by 7 publications

(6 citation statements)

References 9 publications

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“…Although many literatures have carried out relevant studies on probability prediction of wind power [105], photovoltaic power generation [106] and load demand [107], there are still several problems to be solved in probability prediction: 1) Quantile crossover exists in quantile regression method, that is, quantile predicted value does not increase as the corresponding probability value increases [108]. 2) Density leakage in kernel density estimation leads to the problem that the predicted value exceeds the value range of the random variable [109].…”

Section: ) Power Supply and Load Demandmentioning

confidence: 99%

A Review on Optimal Energy Management of Multimicrogrid System Considering Uncertainties

Zhang

2022

IEEE Access

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Microgrid (MG) is one of the most effective solution to integrate distributed renewable energy into power system. However, modern MG has several technical challenges such as migration to multi vector energy system, increasing renewable energy penetration, and the uncertainties that arise from the large-scale incorporation of renewable energy. Recent development of multi-microgrid (MMG) could potentially mitigate these challenges. MMG has obvious advantages in improving the system stability, reliability, economy, and energy efficiency of power grid operation through autonomous management and coordinated control among networked MGs. In the meantime, uncertainties from many elements and operators in the MMG system also pose huge challenges in modeling. This paper will present and review typical architecture of MMG, including physical layer, information layer and application layer. Moreover, this paper will critically review and analyze challenges in uncertainty modelling and solution in MMGs. Finally, the paper will also discuss future research areas and development trends of MMG.

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Section: ) Power Supply and Load Demandmentioning

confidence: 99%

A Review on Optimal Energy Management of Multimicrogrid System Considering Uncertainties

Zhang

2022

IEEE Access

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“…The probabilistic forecast provides prediction intervals in addition to precise values with which the forecast is expected to fall with some predefined confidence level or probability. The additional information about the uncertainty of the prediction is very important to decision-makers such as PVbased electricity market operators [87]. This knowledge is used to provide a precise generation schedule for the dayahead and real-time market.…”

Section: ) Point Forecastmentioning

confidence: 99%

“…A day ahead probabilistic PV power forecasting model based on QRNN (Quartile Regression Neural Network) is proposed in [87] for a 7 MW PV plant in South-East of Spain. The internal settings of the NN and the size of informative inputs are optimized using GA algorithm.…”

Section: ) Point Forecastmentioning

confidence: 99%

A Review of Machine Learning-Based Photovoltaic Output Power Forecasting: Nordic Context

et al. 2022

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“…Regression trees are used in solar forecasting [6]. As inputs, the model employs expected climatic factors from a (NWP) model and real power measurements from photovoltaic (PV) plants [7] to forecast power production. Short-term forecasting of PV power production is done with the help of a Quantile Regression Neural Network (QRNN) [8].…”

Section: Introductionmentioning

confidence: 99%

Hourly and Day Ahead Power Prediction of Building Integrated Semitransparent Photovoltaic System

Kaliappan

Saravanakumar

Karthick

et al. 2021

International Journal of Photoenergy

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The building integrated semitransparent photovoltaic (BISTPV) system is an emerging technology which replaces the conventional building material envelopes and roof. The performance prediction of the BISTPV system places a vital role in the reduction of the energy consumption in the building. In this work, the artificial neural network (ANN) is used to predict the performance of this system by optimizing the important parameter of the feature selection. The Elman neural network (EN) algorithm, feed forward neural network (FN), and generalized regression neural network model (GRN) are investigated in this study. The performance metrics of the errors are analysed such as the root mean square error (RMSE), mean absolute percentage error (MAPE), and mean square root (MSE). According to the findings, the model behaves consistently at the specified time and place in the experiment. Forecasters utilizing neural network models will have better accuracy if they use techniques like EN, FFN, and GRN having the RMSE of 0.25, 0.37, and 0.45, respectively.

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Day-Ahead Probabilistic Photovoltaic Power Forecasting Models Based on Quantile Regression Neural Networks

Cited by 7 publications

References 9 publications

A Review on Optimal Energy Management of Multimicrogrid System Considering Uncertainties

A Review on Optimal Energy Management of Multimicrogrid System Considering Uncertainties

A Review of Machine Learning-Based Photovoltaic Output Power Forecasting: Nordic Context

Hourly and Day Ahead Power Prediction of Building Integrated Semitransparent Photovoltaic System

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