Probabilistic Solar Irradiance Forecasting by Conditioning Joint Probability Method and Its Application to Electric Power Trading

Kakimoto, Mitsuru; Endoh, Yusuke; Shin, Hiromasa; Ikeda, Ryosaku; Kusaka, Hiroyuki

doi:10.1109/tste.2018.2858777

Cited by 42 publications

(14 citation statements)

References 44 publications

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“…If we directly add them, lo is triggered with 1(1/2+1/2), which is incorrect as there are probabilities to run action l j or l l Notice, in all-trigger, successive actions are triggered, and the sum of all edges' weights is bigger than 1. This makes it difficult to use traditional methods (such as Markov process [25] or the joint probability method to analyze [24]) as they require that the sum of its outgoing edge (its triggered actions) weight is 1.…”

Section: B Trigger Typesmentioning

confidence: 99%

Quantitative Cooperation Analysis among Cross-chain Smart Contracts

Hong¹

2021

Preprint

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<div>In cross-chain scenarios, there are different blockchains, which need to cooperate. Cooperation among different blockchains is done by smart contracts that work together to complete cross-chain tasks. When numerous cooperative smart contracts are involved, smart contracts form a complex interaction network, which makes it difficult to evaluate the cooperation. It needs a common model to quantitatively analyze the cross-chain cooperation of associated smart contracts. In this paper, we model the cooperation among smart contracts as conditions and their corresponding actions, the condition-trigger model. Then we propose the method to calculate the cooperation probabilities by the graph weight. As the edge weight lacks the information of interaction probabilities, we introduce the dimension of the edge weight to calculate the probabilities. Finally, we verify the proposed condition-trigger model and its different types. It demonstrates that our proposed methods can effectively analyze the cross-chain cooperation among smart contracts.</div>

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Section: B Trigger Typesmentioning

confidence: 99%

Quantitative Cooperation Analysis among Cross-chain Smart Contracts

Hong¹

2021

Preprint

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“…A composite approach that includes quantile regression, Bayesian approach, and Markov chain is presented in [25]. To predict the PDF of solar irradiance, NWP as a physical method and Bayes rule are used in [26]. An indirect PDF forecasting method based on support vector quantile regression and fuzzy information granulation is presented for wind and solar power generations in [27].…”

Section: Literature Reviewmentioning

confidence: 99%

Deep learning architecture for direct probability density prediction of small‐scale solar generation

Afrasiabi

Mohammadi

Rastegar

et al. 2020

IET Generation, Transmission & Distribution

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“…For example, in [12], the application of the analog ensemble method for the prediction of the solar power plant energy output was described, and in [13], its modification was analyzed for open-source meteorological data. The application of numerical weather prediction (NWP) algorithms for the evaluation of the magnitude of solar irradiation is described in [14]. The implementation of the network of weather monitoring systems allows one to increase the accuracy of such forecasting, an example of which is presented in [15].…”

Section: Introductionmentioning

confidence: 99%

Industry Experience of Developing Day-Ahead Photovoltaic Plant Forecasting System Based on Machine Learning

et al. 2020

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This article highlights the industry experience of the development and practical implementation of a short-term photovoltaic forecasting system based on machine learning methods for a real industry-scale photovoltaic power plant implemented in a Russian power system using remote data acquisition. One of the goals of the study is to improve photovoltaic power plants generation forecasting accuracy based on open-source meteorological data, which is provided in regular weather forecasts. In order to improve the robustness of the system in terms of the forecasting accuracy, we apply newly derived feature introduction, a factor obtained as a result of feature engineering procedure, characterizing the relationship between photovoltaic power plant energy production and solar irradiation on a horizontal surface, thus taking into account the impacts of atmospheric and electrical nature. The article scrutinizes the application of different machine learning algorithms, including Random Forest regressor, Gradient Boosting Regressor, Linear Regression and Decision Trees regression, to the remotely obtained data. As a result of the application of the aforementioned approaches together with hyperparameters, tuning and pipelining of the algorithms, the optimal structure, parameters and the application sphere of different regressors were identified for various testing samples. The mathematical model developed within the framework of the study gave us the opportunity to provide robust photovoltaic energy forecasting results with mean accuracy over 92% for mostly-sunny sample days and over 83% for mostly cloudy days with different types of precipitation.

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Probabilistic Solar Irradiance Forecasting by Conditioning Joint Probability Method and Its Application to Electric Power Trading

Cited by 42 publications

References 44 publications

Quantitative Cooperation Analysis among Cross-chain Smart Contracts

Quantitative Cooperation Analysis among Cross-chain Smart Contracts

Deep learning architecture for direct probability density prediction of small‐scale solar generation

Industry Experience of Developing Day-Ahead Photovoltaic Plant Forecasting System Based on Machine Learning

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