Titlebook: Data Analytics in Power Markets; Qixin Chen,Hongye Guo,Yi Wang Book 2021 Science Press 2021 Power markets.bidding strategy.machine learnin

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Introduction to Power Market Data,matic changes for system operators, generation companies, and electricity consumers. The operation of power markets constantly produces valuable market data which can support the decision of both market organizers and market participants. This chapter presents an introduction to power market data. F

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Load Data Cleaning and Forecastingally due to cyber attacks and equipment failures. The bad data may result in bias for load forecasting and other data analytic applications. This chapter proposes a novel bad data identification and repairing method for load profiles. In the first stage, the Singular Value Thresholding (SVT) algorit

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Monthly Electricity Consumption Forecastingctricity consumption. To improve the accuracy and applicability of mid-term, especially monthly, electricity consumption forecasting, a novel monthly electricity consumption forecasting framework (denoted as SAS-SVECM for short) based on vector error correction model (VECM) and self-adaptive screeni

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Day-Ahead Electricity Price Forecastingt. Most electricity market organizers in the world release the data of LMP along with its three components, i.e., the energy, congestion, and loss components. The series of the three components have their own patterns and driving factors, and can be utilized to improve the accuracy of LMP forecastin

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