How to model European electricity load profiles using artificial neural networks

Christian Behm, Lars Nolting and Aaron Praktiknjo

Applied Energy, 2020, vol. 277, issue C, No S030626192031076X

Abstract: We present a method to create synthetic, weather-dependent, annual electricity load profiles for European countries in hourly resolution using artificial neural networks as a necessary basis for long-term forecasts. To this end, we train fully connected dense artificial neural networks with 5 hidden layers and 1,024 hidden nodes per layer using historic data for Germany from 2006 to 2015. Input parameters used in the model comprise calendrical information, annual peak loads and weather data. We benchmark our results against the current state-of-the-art method to generate synthetic load profiles used in mid-term adequacy forecasts published by the European Network of Transmission System Operators (entso-e). For validation year 2016, our approach shows a mean absolute percentage error of 2.8%, whereas the method as used by entso-e shows an average error of 4.8%. We then conduct forecasts for Germany, Sweden, Spain, and France using our synthetic load profiles for scenario year 2025 to demonstrate their pan-European applicability. Finally, we assess parameter variations that demonstrate high influences of outdoor temperatures and wind speed on the electricity load. Our approach can help to increase prediction accuracy of future electricity loads as electricity load profiles are a necessary input for these forecasts.

Keywords: Artificial Intelligence; Artificial neural networks; Energy system modeling; Electricity load; Security of electricity supply; Machine learning (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (21)

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DOI: 10.1016/j.apenergy.2020.115564

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