Effective prediction model for Hungarian small‐scale solar power output

Abedinia, Oveis; Raisz, Dávid; Amjady, Nima

doi:10.1049/iet-rpg.2017.0165

Cited by 69 publications

(34 citation statements)

References 31 publications

(56 reference statements)

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“…A classification can be made based on how the weather data are utilized as input. Three categories were observed, studies that only use weather forecast [5][6][7][8][9][10][11][12][13][14][15][16][17][18], those that use only weather observation [19][20][21][22][23][24][25], and those that use both forms of weather data [1,[26][27][28][29][30]. In the first category, planning and projection is required before the actual generation of solar energy, but they are highly correlated with the errors that meteorological stations can make in the forecasting.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison Analysis of Machine Learning Techniques for Photovoltaic Prediction Using Weather Sensor Data

Carrera

Kim

2020

Sensors

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Over the past few years, solar power has significantly increased in popularity as a renewable energy. In the context of electricity generation, solar power offers clean and accessible energy, as it is not associated with global warming and pollution. The main challenge of solar power is its uncontrollable fluctuation since it is highly depending on other weather variables. Thus, forecasting energy generation is important for smart grid operators and solar electricity providers since they are required to ensure the power continuity in order to dispatch and properly prepare to store the energy. In this study, we propose an efficient comparison framework for forecasting the solar power that will be generated 36 h in advance from Yeongam solar power plant located in South Jeolla Province, South Korea. The results show a comparative analysis of the state-of-the-art techniques for solar power generation.

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Section: Literature Reviewmentioning

confidence: 99%

Comparison Analysis of Machine Learning Techniques for Photovoltaic Prediction Using Weather Sensor Data

Carrera

Kim

2020

Sensors

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“…Constraints (3) and (4) identify, for each switching operation, the feasibility of the instantaneous state reached just before switching occurrence. Constraints (5) and (6) are analogous to (3), (4) for the post-switching states of…”

Section: Problem Formulationmentioning

confidence: 99%

“…However, as described in [1], ''the redundancy built into the transmission network in order to handle a multitude of contingencies over a long planning horizon can, in the short run, increase operating costs''. In recent years, the proportion of renewable energies including wind and photovoltaic is growing rapidly on the supply side [2,3]. On the load side, the dispatchable loads play more and more important roles in power markets [4].…”

Section: Introductionmentioning

confidence: 99%

Network transition security for transmission switching

Tian

Wang

et al. 2019

J. Mod. Power Syst. Clean Energy

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The smart grid with flexible topologies receives intensive attention recently. Transmission switching (TS) alters the power system topology during operation, and has been demonstrated for the advantage of economic and secure operation of power systems. TS includes a chain of sequential switching actions which bring disturbances to the system if the switching actions are not properly designed. Unfortunately, it is not considered or well-studied in existing works. In this paper, a new multi-period TS model that considers the transition security and a two-stage iterative method are proposed. In the TS model, we take into account the fact that only one line is permitted to switch up or down at a time and the security of each switching action is considered. The proposed iterative solution makes the TS model more tractable under AC framework. Case studies on a 6-bus system and the IEEE 57-bus test system have varified the effectiveness of the proposed model. Numerical results show that: À the consideration of transition security of TS is essential;`the transition path is directly related to secure and fast the transmission switching;´the proposed model and solution method give an effective way to determine the switching sequence and switching timing under transition security criteria.

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“…With rapid developments of statistical learning methods over the last decade, many studies have adopted this data-driven approach to developing PV prediction models [4]. Lastly, hybrid methods [5,6] apply not only statistical methods but also other methods, such as mathematical optimization or signal processing.…”

Section: Introductionmentioning

confidence: 99%

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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Effective prediction model for Hungarian small‐scale solar power output

Cited by 69 publications

References 31 publications

Comparison Analysis of Machine Learning Techniques for Photovoltaic Prediction Using Weather Sensor Data

Comparison Analysis of Machine Learning Techniques for Photovoltaic Prediction Using Weather Sensor Data

Network transition security for transmission switching

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

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