Hybrid solar forecasting method uses satellite imaging and ground telemetry as inputs to ANNs

Marquez, Ricardo; Pedro, Hugo T.C.; Coimbra, Carlos F.M.

doi:10.1016/j.solener.2013.02.023

Cited by 155 publications

(69 citation statements)

References 20 publications

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“…The former methods are mainly used for intra-hour forecasting, while the latter features forecast horizons of tens of minutes to up to 6 h [18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

et al. 2017

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Accurate solar photovoltaic (PV) power forecasting is an essential tool for mitigating the negative effects caused by the uncertainty of PV output power in systems with high penetration levels of solar PV generation. Weather classification based modeling is an effective way to increase the accuracy of day-ahead short-term (DAST) solar PV power forecasting because PV output power is strongly dependent on the specific weather conditions in a given time period. However, the accuracy of daily weather classification relies on both the applied classifiers and the training data. This paper aims to reveal how these two factors impact the classification performance and to delineate the relation between classification accuracy and sample dataset scale. Two commonly used classification methods, K-nearest neighbors (KNN) and support vector machines (SVM) are applied to classify the daily local weather types for DAST solar PV power forecasting using the operation data from a grid-connected PV plant in Hohhot, Inner Mongolia, China. We assessed the performance of SVM and KNN approaches, and then investigated the influences of sample scale, the number of categories, and the data distribution in different categories on the daily weather classification results. The simulation results illustrate that SVM performs well with small sample scale, while KNN is more sensitive to the length of the training dataset and can achieve higher accuracy than SVM with sufficient samples.

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“…The former methods are mainly used for intra-hour forecasting, while the latter features forecast horizons of tens of minutes to up to 6 h [18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

et al. 2017

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“…Best Model (4) = All-months dataset + Normalize (A) + Parm10_08 + Orig14ins (6) In the graphical illustration, the month of October is chosen for comparison of all SVRs models with other combined models as shown in Fig.6. For the aggregation of daily RMSEs over the whole month, it is obvious the ensemble forecasts (black line) has a lower RMSE than SVR models and the simple average combining method (dark blue line).…”

Section: Results and Evaluationmentioning

confidence: 99%

Random forest ensemble of support vector regression models for solar power forecasting

Abuella

Chowdhury

2017

2017 IEEE Power &Amp; Energy Society Innovative Smart Grid Technologies Conference (ISGT)

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Abstract-To mitigate the uncertainty of variable renewable resources, two off-the-shelf machine learning tools are deployed to forecast the solar power output of a solar photovoltaic system. The support vector machines generate the forecasts and the random forest acts as an ensemble learning method to combine the forecasts. The common ensemble technique in wind and solar power forecasting is the blending of meteorological data from several sources. In this study though, the present and the past solar power forecasts from several models, as well as the associated meteorological data, are incorporated into the random forest to combine and improve the accuracy of the day-ahead solar power forecasts. The performance of the combined model is evaluated over the entire year and compared with other combining techniques.

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“…Marquez et al [32] predict GHI at temporal horizons of 30, 60, 90, and 120 minutes. They use a hybrid method that combines information from processed satellite images with ANN.…”

Section: Skill Scores Comparisonmentioning

confidence: 99%

“…The input variables for Models 1 and 2 are data from satellite images and lagged GHI data, respectively. Although Marquez et al [32] and Inman et al [33] examine forecasts at 30-minute intervals, it is nevertheless helpful to compare the performance of their model to our VAR using the same number of forecasting steps. For our VAR model, the skill scores of solar forecasts between one and four time steps ahead are 0.08, 0.18, 0.31, and 0.43.…”

Section: Skill Scores Comparisonmentioning

confidence: 99%

A vector autoregression weather model for electricity supply and demand modeling

Liu

Roberts

Sioshansi

2018

J. Mod. Power Syst. Clean Energy

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Weather forecasting is crucial to both the demand and supply sides of electricity systems. Temperature has a great effect on the demand side. Moreover, solar and wind are very promising renewable energy sources and are, thus, important on the supply side. In this paper, a large vector autoregression (VAR) model is built to forecast three important weather variables for 61 cities around the United States. The three variables at all locations are modeled as response variables. Lag terms are used to capture the relationship between observations in adjacent periods and daily and annual seasonality are modeled to consider the correlation between the same periods in adjacent days and years. We estimate the VAR model with 16 years of hourly historical data and use two additional years of data for out-of-sample validation. Forecasts of up to six-hours-ahead are generated with good forecasting performance based on mean absolute error, root mean square error, relative root mean square error, and skill scores. Our VAR model gives forecasts with skill scores that are more than double the skill scores of other forecasting models in the literature. Our model also provides forecasts that outperform persistence forecasts by between 6% and 80% in terms of mean absolute error. Our results show that the proposed time series approach is appropriate for very short-term forecasting of hourly solar radiation, temperature, and wind speed.

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Hybrid solar forecasting method uses satellite imaging and ground telemetry as inputs to ANNs

Cited by 155 publications

References 20 publications

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

Random forest ensemble of support vector regression models for solar power forecasting

A vector autoregression weather model for electricity supply and demand modeling

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