A Concept-Drift Based Predictive-Analytics Framework: Application for Real-Time Solar Irradiance Forecasting

Wojtkiewicz, Jessica; Katragadda, Satya; Gottumukkala, Raju

doi:10.1109/bigdata.2018.8622216

Cited by 12 publications

(10 citation statements)

References 9 publications

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“…On days where there is no cloud cover, both models performed exceptionally; however, on days with cloud cover, both models fail to accurately predict future solar irradiance. Rather than having one static model to predict non-cloudy and cloudy days, we could potentially use ensemble methods or concept drift methods to reduce over-fitting on days where there is high variability in cloud cover [34]. Moreover, more research is needed in not only reducing the forecast error, but also maximizing the utility of irradiance forecasts.…”

Section: Discussionmentioning

confidence: 99%

Hour-Ahead Solar Irradiance Forecasting Using Multivariate Gated Recurrent Units

et al. 2019

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Variation in solar irradiance causes power generation fluctuations in solar power plants. Power grid operators need accurate irradiance forecasts to manage this variability. Many factors affect irradiance, including the time of year, weather and time of day. Cloud cover is one of the most important variables that affects solar power generation, but is also characterized by a high degree of variability and uncertainty. Deep learning methods have the ability to learn long-term dependencies within sequential data. We investigate the application of Gated Recurrent Units (GRU) to forecast solar irradiance and present the results of applying multivariate GRU to forecast hourly solar irradiance in Phoenix, Arizona. We compare and evaluate the performance of GRU against Long Short-Term Memory (LSTM) using strictly historical solar irradiance data as well as the addition of exogenous weather variables and cloud cover data. Based on our results, we found that the addition of exogenous weather variables and cloud cover data in both GRU and LSTM significantly improved forecasting accuracy, performing better than univariate and statistical models.

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Section: Discussionmentioning

confidence: 99%

Hour-Ahead Solar Irradiance Forecasting Using Multivariate Gated Recurrent Units

et al. 2019

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“…Having a reliable forecast of solar irradiance (SIr) is of great importance, due to its effect on the design of photovoltaic systems and measuring solar energy production [90,91]. Figure 1 shows solar radiation on a photovoltaic module installed on the earth.…”

Section: Introductionmentioning

confidence: 99%

An Innovative Metaheuristic Strategy for Solar Energy Management Through a Neural Framework

Moayedi¹,

Mosavi²

2021

Preprint

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Proper management of solar energy, as an effective renewable source, is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO) is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for non-linearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO (i.e., NPop, R_rate, Ps_rate, P_field, and N_field) are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development.

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“…The authors also concluded that GRU is less resource-intensive and faster compared to LSTM. The proposed work builds on our prior work [28], where we analyzed the performance of LSTM and GRU for GHI forecasting with and without using exogenous variables for one hour ahead forecasting [29]. Our observation in our prior work is that including weather variables particularly cloud cover significantly improved forecasting performance for both LSTM and GRU compared to univariate models.…”

Section: Introductionmentioning

confidence: 99%

Direct Normal Irradiance Forecasting Using Multivariate Gated Recurrent Units

et al. 2020

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Power grid operators rely on solar irradiance forecasts to manage uncertainty and variability associated with solar power. Meteorological factors such as cloud cover, wind direction, and wind speed affect irradiance and are associated with a high degree of variability and uncertainty. Statistical models fail to accurately capture the dependence between these factors and irradiance. In this paper, we introduce the idea of applying multivariate Gated Recurrent Units (GRU) to forecast Direct Normal Irradiance (DNI) hourly. The proposed GRU-based forecasting method is evaluated against traditional Long Short-Term Memory (LSTM) using historical irradiance data (i.e., weather variables that include cloud cover, wind direction, and wind speed) to forecast irradiance forecasting over intra-hour and inter-hour intervals. Our evaluation on one of the sites from Measurement and Instrumentation Data Center indicate that both GRU and LSTM improved DNI forecasting performance when evaluated under different conditions. Moreover, including wind direction and wind speed can have substantial improvement in the accuracy of DNI forecasts. Besides, the forecasting model can accurately forecast irradiance values over multiple forecasting horizons.

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A Concept-Drift Based Predictive-Analytics Framework: Application for Real-Time Solar Irradiance Forecasting

Cited by 12 publications

References 9 publications

Hour-Ahead Solar Irradiance Forecasting Using Multivariate Gated Recurrent Units

Hour-Ahead Solar Irradiance Forecasting Using Multivariate Gated Recurrent Units

An Innovative Metaheuristic Strategy for Solar Energy Management Through a Neural Framework

Direct Normal Irradiance Forecasting Using Multivariate Gated Recurrent Units

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