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Machine Learning in Energy Forecasts with an Application to High Frequency Electricity Consumption Data
Forecasting plays an essential role in energy economics. With new challenges and use cases in the energy system, forecasts have to meet more complex requirements, such as increasing temporal and spatial resolution of data. The concept of machine learning can meet these requirements by providing diff...
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| 出版年: | MAGKS - Joint Discussion Paper Series in Economics (Band 35-2021) |
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| 主要な著者: | , , |
| フォーマット: | 論文 |
| 言語: | 英語 |
| 出版事項: |
Philipps-Universität Marburg
2021
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| 主題: | |
| オンライン･アクセス: | PDFフルテキスト |
| タグ: |
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| 要約: | Forecasting plays an essential role in energy economics. With new challenges and use cases in the energy system, forecasts have to meet more complex requirements, such as increasing temporal and spatial resolution of data. The concept of machine learning can meet these requirements by providing different model approaches and a standardized process of model selection. This paper provides a concise and comprehensible introduction to the topic by discussing the concept of machine learning in the context of energy economics and presenting an exemplary application to electricity load data. For this, we introduce and demonstrate the structured machine learning process containing the preparation, model selection and test of forecast models. This process is intended to serve as a general guideline for energy economists and practitioners who need to apply sophisticated forecast models. |
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| 物理的記述: | 29 Seiten |
| ISSN: | 1867-3678 |
| DOI: | 10.17192/es2024.0706 |