Comparison of short-term electrical load forecasting methods for different building types
| Autor: | Daniel Braun, Friedhelm Schwenker, David Fischer, Antonia Lenders, Arne Groß |
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| Přispěvatelé: | Publica |
| Rok vydání: | 2021 |
| Předmět: |
Leistungselektronik
Artificial neural network Electrical load Computer Networks and Communications Computer science Netze und Intelligente Systeme Energy Engineering and Power Technology forecasting Energy industries. Energy policy. Fuel trade Regression Term (time) Task (project management) electric load profile machine learning Transformation (function) intelligentes Netz Statistics Benchmark (computing) Key (cryptography) HD9502-9502.5 Information Systems |
| Zdroj: | Energy Informatics, Vol 4, Iss S3, Pp 1-16 (2021) |
| ISSN: | 2520-8942 |
| DOI: | 10.1186/s42162-021-00172-6 |
| Popis: | The transformation of the energy system towards volatile renewable generation, increases the need to manage decentralized flexibilities more efficiently. For this, precise forecasting of uncontrollable electrical load is key. Although there is an abundance of studies presenting innovative individual methods for load forecasting, comprehensive comparisons of popular methods are hard to come across.In this paper, eight methods for day-ahead forecasts of supermarket, school and residential electrical load on the level of individual buildings are compared. The compared algorithms came from machine learning and statistics and a median ensemble combining the individual forecasts was used.In our examination, nearly all the studied methods improved forecasting accuracy compared to the naïve seasonal benchmark approach. The forecast error could be reduced by up to 35% compared to the benchmark. From the individual methods, the neural networks achieved the best results for the school and supermarket buildings, whereas the k-nearest-neighbor regression had the lowest forecasting error for households. The median ensemble narrowly yielded a lower forecast error than all individual methods for the residential and school category and was only outperformed by a neural network for the supermarket data. However, this slight increase in performance came at the cost of a significantly increased computation time. Overall, identifying a single best method remains a challenge specific to the forecasting task. |
| Databáze: | OpenAIRE |
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