On the Use of Maximum Likelihood and Input Data Similarity to Obtain Prediction Intervals for Forecasts of Photovoltaic Power Generation

Fonseca, Joao Gari da Silva; Oozeki, Takashi; Ohtake, Hideaki; Takashima, Takumi; Ogimoto, Kazuhiko

doi:10.5370/jeet.2015.10.3.1342

Cited by 37 publications

(11 citation statements)

References 5 publications

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“…He et al utilize the support vector machine for the QR with the fuzzy information granulation [14]. On the other hand, as the parametric approach, J. Fonseca Jr. et al estimated the confidence interval with assuming probability distribution function by comparing the input data with the historical data of the previous 60 days [15]. However, the accuracy of the NWP presumably become the bottleneck of the deterministic solar power forecasting and the probabilistic one.…”

Section: Introductionmentioning

confidence: 99%

Regional Solar Irradiance Forecast for Kanto Region by Support Vector Regression Using Forecast of Meso-Ensemble Prediction System

et al. 2021

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From the perspective of stable operation of the power transmission system, the transmission system operators (TSO) needs to procure reserve adjustment power at the stage of the previous day based on solar power forecast information from global horizontal irradiance (GHI). Because the reserve adjustment power is determined based on information on major outliers in past forecasts, reducing the maximum forecast error in addition to improving the average forecast accuracy is extremely important from the perspective of grid operation. In the past, researchers have proposed various methods combining the numerical weather prediction (NWP) and machine learning techniques for the one day-ahead solar power forecasting, but the accuracy of NWP has been a bottleneck issue. In recent years, the development of the ensemble prediction system (EPS) forecasts based on probabilistic approaches has been promoted to improve the accuracy of NWP, and in Japan, EPS forecasts in the mesoscale domain, called mesoscale ensemble prediction system (MEPS), have been distributed by the Japan Meteorological Agency (JMA). The use of EPS as a machine learning model is expected to improve the maximum forecast error, as well as the accuracy, since the predictor can utilize various weather scenarios as information. The purpose of this study is to examine the effect of EPS on the GHI prediction and the structure of the machine learning model that can effectively use EPS. In this study, we constructed the support vector regression (SVR)-based predictors with multiple network configurations using MEPS as input and evaluated the forecast error of the Kanto region GHI by each model. Through the comparison of the prediction results, it was shown that the machine learning model can achieve average accuracy improvement while reducing the maximum prediction error by MEPS, and knowledge was obtained on how to effectively provide EPS information to the predictor. In addition, machine learning was found to be useful in improving the systematic error of MEPS.

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Section: Introductionmentioning

confidence: 99%

Regional Solar Irradiance Forecast for Kanto Region by Support Vector Regression Using Forecast of Meso-Ensemble Prediction System

et al. 2021

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“…In [12–14], the forecast errors are assumed to be Gaussian distributed. Other than Gaussian distribution, Laplace distribution is also evaluated in [15] to model the forecast errors. In these approaches, point values are firstly forecasted and then their uncertainties are evaluated.…”

Section: Introductionmentioning

confidence: 99%

Randomised learning‐based hybrid ensemble model for probabilistic forecasting of PV power generation

Liu

2020

IET Generation, Transmission & Distribution

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“…Fatemi et al [27] proposed two parametric probability prediction methods for predicting solar irradiance by β-distribution and bilateral power distribution, effectively predicting solar irradiance and accurately describing its stochastic characteristics. Fonseca et al [28] assumed the prediction error distribution as the normal distribution and the Laplacian distribution. The probability distribution of the generated power and the confidence interval value at different confidence levels was then obtained by the maximum likelihood estimation method.…”

Section: Introductionmentioning

confidence: 99%

Improved Probability Prediction Method Research for Photovoltaic Power Output

2019

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Due to solar radiation and other meteorological factors, photovoltaic (PV) output is intermittent and random. Accurate and reliable photovoltaic power prediction can improve the stability and safety of grid operation. Compared to solar power point prediction, probabilistic prediction methods can provide more information about potential uncertainty. Therefore, this paper first proposes two kinds of photovoltaic output probability prediction models, which are improved sparse Gaussian process regression model (IMSPGP), and improved least squares support vector machine error prediction model (IMLSSVM). In order to make full use of the advantages of the different models, this paper proposes a combined forecasting method with divided-interval and variable weights, which divides one day into four intervals. The models are combined by the optimal combination method in each interval. The simulation results show that IMSPGP and IMLSSVM have better prediction accuracy than the original models, and the combination model obtained by the combination method proposed in this paper further improves the prediction performance.

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On the Use of Maximum Likelihood and Input Data Similarity to Obtain Prediction Intervals for Forecasts of Photovoltaic Power Generation

Cited by 37 publications

References 5 publications

Regional Solar Irradiance Forecast for Kanto Region by Support Vector Regression Using Forecast of Meso-Ensemble Prediction System

Regional Solar Irradiance Forecast for Kanto Region by Support Vector Regression Using Forecast of Meso-Ensemble Prediction System

Randomised learning‐based hybrid ensemble model for probabilistic forecasting of PV power generation

Improved Probability Prediction Method Research for Photovoltaic Power Output

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