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WIREs Data Mining Knowl Discov
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Energy forecasting tools and services

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The increasing complexity of the power grid and the continuous integration of volatile renewable energy systems on all aspects of it have made more precise forecasts of both energy supply and demand necessary for the future Smart Grid. Yet, the ever increasing volume of tools and services makes it difficult for users (e.g., energy utility companies) and researchers to obtain even a general sense of what each tool or service offers. The present contribution provides an overview and categorization of several energy‐related forecasting tools and services (specifically for load and volatile renewable power), as well as general information regarding principles of time series, load, and volatile renewable power forecasting. WIREs Data Mining Knowl Discov 2018, 8:e1235. doi: 10.1002/widm.1235 This article is categorized under: Application Areas > Business and Industry Application Areas > Data Mining Software Tools Technologies > Prediction
Examples of load forecasting models’ applications depending on their forecast horizon. The figure is based on the article given by Raza et al.
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Heat maps depicting (a) normalized average daily household electrical loads across several months and (b) normalized average daily household electrical loads across the different weekdays.
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Maturity of energy‐related point and probabilistic forecasting based on the figure presented by Hong et al. STL: short‐term (2 weeks ahead or shorter) load forecasting; LTL: long‐term (a few months to a few decades ahead) load forecasting; PV: photovoltaic power forecasting; W: wind power forecasting.
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Different forecasting model types. (a) White‐box models: created using known relations, expert knowledge, so forth; (b) Black‐box models: created utilizing pure data mining techniques; and (c) Gray‐box models: created through the combination of white and black‐box models.
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Examples of photovoltaic power forecasting models’ applications depending on their forecast horizon, based on a depiction by Wan et al.
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