Weather-based solar energy prediction

Detyniecki, Marcin; Marsala, Christophe; Krishnan, Ashwati; Siegel, Mel

doi:10.1109/fuzz-ieee.2012.6251145

Cited by 19 publications

(10 citation statements)

References 14 publications

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“…Techniques that use Long Short-Term Memory (LSTM), a time-series analysis method for weather data [39,40], as well as techniques that use both the past and current weather data, have also been proposed. Other studies introduce methods that use the adaptive linear time series model, and a technique for applying both past data and forecasts to the fuzzy decision tree model [41][42][43].…”

Section: Solar Power Estimation and Inverter Efficiency Analysismentioning

confidence: 99%

Inverter Efficiency Analysis Model Based on Solar Power Estimation Using Solar Radiation

Park¹,

Hong²,

Lim³

et al. 2020

Processes

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The photovoltaic (PV) industry is an important part of the renewable energy industry. With the growing use of PV systems, interest in their operation and maintenance (O&M) is increasing. In this regard, analyses of power generation efficiency and inverter efficiency are very important. The first step in efficiency analysis is solar power estimation based on environment sensor data. In this study, solar power was estimated using a univariate linear regression model. The estimated solar power data were cross-validated with the actual solar power data obtained from the inverter. The results provide information on the power generation efficiency of the inverter. The linear estimation model developed in this study was validated using a single PV system. It is possible to apply the coefficients presented in this study to other PV systems, even though the nature and error rates of the collected data may vary depending on the inverter manufacturer. To apply the proposed model to PV systems with different power generation capacities, reconstructing the model according to the power generation capacity is necessary.

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Section: Solar Power Estimation and Inverter Efficiency Analysismentioning

confidence: 99%

Inverter Efficiency Analysis Model Based on Solar Power Estimation Using Solar Radiation

Park¹,

Hong²,

Lim³

et al. 2020

Processes

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“…Several studies have noted the impact of meteorology on PV generation; for example, Nonnenmacher et al (2014) find that fog can impact PV output readings, and Yang et al (2014) note that solar irradiance forecast accuracy is affected by the meteorological conditions of different seasons. Almost all studies that attempt this restrict themselves to categorizing the basic weather type; for example, several studies used symbolic weather categories (such as ''sunny,'' ''fair,'' and ''showers'') from numerical weather prediction (NWP) information to predict PV output (Chel and Tiwari 2011;Shi et al 2011;Detyniecki et al 2012). Almost all studies that attempt this restrict themselves to categorizing the basic weather type; for example, several studies used symbolic weather categories (such as ''sunny,'' ''fair,'' and ''showers'') from numerical weather prediction (NWP) information to predict PV output (Chel and Tiwari 2011;Shi et al 2011;Detyniecki et al 2012).…”

Section: B Motivationmentioning

confidence: 99%

Categorizing the Meteorological Origins of Critical Ramp Events in Collective Photovoltaic Array Output

Wellby

Engerer²

2016

Journal of Applied Meteorology and Climatology

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Photovoltaic (PV) solar power use is increasing globally. The Australian Capital Territory (ACT) has legislated a renewable energy target of 90% by 2020; to reach this target, use of distributed PV solar arrays is expected to increase. Cloud cover can cause the power output of PV installations to rapidly increase or decrease, resulting in ACT-wide collective ramp events. Accurate forecasts of when the ramp events will occur are needed for electricity providers to plan for these abrupt output changes and to ensure that electricity supplies remain stable. This paper categorizes the weather events that cause changes in the output of rooftop PV arrays in the ACT, providing a foundation for future PV output forecasting to be based on weather event identification. This paper identifies citywide collective ramp events, which occur when a 60% change in collective PV power output (with respect to the clear-sky potential) is experienced within 60 min. Such events are termed critical collective ramp events. Throughout the period between January 2012 and July 2014, 34 critical ramp events occurred. Eighteen of these events were positive collective ramp events, caused most frequently by Australian northwest cloud bands and radiation fog dissipation. Sixteen negative collective ramp events were recorded, and they were caused most frequently by the passage of cold fronts and thunderstorms. The categories developed herein will make it possible to improve short-term solar forecasting methods and to enable meteorologists to contribute to forecasting critical events.

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“…Nonetheless, weather forecasts have some issues in terms of data quality. First, they are not exactly accurate, and the weather agencies typically announce values under concerns of risk averseness [24]. It may limit the performance of resulting predictive models that rely only on weather forecasts.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the pros and cons of weather observations and weather forecasts, we believe that these two data sources should be utilized in a complementary manner. In fact, a few studies [24,25] use both observations and forecasts for prediction. Bacher et al [25] propose an adaptive linear time series model whose autoregressive component for recent solar irradiation is supplemented by an exogenous input of weather forecasts.…”

Section: Introductionmentioning

confidence: 99%

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A Two-Step Approach to Solar Power Generation Prediction Based on Weather Data Using Machine Learning

2019

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Photovoltaic systems have become an important source of renewable energy generation. Because solar power generation is intrinsically highly dependent on weather fluctuations, predicting power generation using weather information has several economic benefits, including reliable operation planning and proactive power trading. This study builds a model that predicts the amounts of solar power generation using weather information provided by weather agencies. This study proposes a two-step modeling process that connects unannounced weather variables with announced weather forecasts. The empirical results show that this approach improves a base approach by wide margins, regardless of types of applied machine learning algorithms. The results also show that the random forest regression algorithm performs the best for this problem, achieving an R-squared value of 70.5% in the test data. The intermediate modeling process creates four variables, which are ranked with high importance in the post-analysis. The constructed model performs realistic one-day ahead predictions.

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Weather-based solar energy prediction

Cited by 19 publications

References 14 publications

Inverter Efficiency Analysis Model Based on Solar Power Estimation Using Solar Radiation

Inverter Efficiency Analysis Model Based on Solar Power Estimation Using Solar Radiation

Categorizing the Meteorological Origins of Critical Ramp Events in Collective Photovoltaic Array Output

A Two-Step Approach to Solar Power Generation Prediction Based on Weather Data Using Machine Learning

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