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Accuracy Analysis of Selected Time Series and Machine Learning Methods for Smart Cities based on Estonian Electricity Consumption Forecast

  • Increasing shares of renewable energy sources in combination with rising popularity of demand response applications and flexibility programs forces higher awareness for production and consumption balancing. Accurate models for forecasting are not just necessary for PV- or wind power sources in smart cities, but also the prediction of loads respectively consumption, which can be based on time series analysis or machine learning methods. Three of those methods, namely a linear regression (LM), a long short-term memory network (LSTM) and a neural network model (NN), have been selected to see their performance on predicting the load of a large smart city on the example of the Estonian electricity consumption data. Hourly data of the year 2019 was used as training data to predict the first 20 days of 2020. For this kind of prediction, the LM showed the lowest root mean square error (RMSE) and had the lowest computational time. The neural network was slightly less accurate. The LSTM showed the worst performance in terms of accuracy andIncreasing shares of renewable energy sources in combination with rising popularity of demand response applications and flexibility programs forces higher awareness for production and consumption balancing. Accurate models for forecasting are not just necessary for PV- or wind power sources in smart cities, but also the prediction of loads respectively consumption, which can be based on time series analysis or machine learning methods. Three of those methods, namely a linear regression (LM), a long short-term memory network (LSTM) and a neural network model (NN), have been selected to see their performance on predicting the load of a large smart city on the example of the Estonian electricity consumption data. Hourly data of the year 2019 was used as training data to predict the first 20 days of 2020. For this kind of prediction, the LM showed the lowest root mean square error (RMSE) and had the lowest computational time. The neural network was slightly less accurate. The LSTM showed the worst performance in terms of accuracy and computational time. Thus, LSTM is not the preferred method for this kind of prediction and the recommendation for forecasting such loads would be a LM because the RMSE and computational effort needed are lower than for a NNshow moreshow less

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Metadaten
Author:Tobias Häring, Roya Ahmadiahangar, Argo RosinORCiD, Tarmo Korotko, Helmuth Biechl
DOI:https://doi.org/10.1109/CPE-POWERENG48600.2020.9161690
Identifier:978-1-7281-4218-0 OPAC HS OPAC extern
Parent Title (English):2020 IEEE 14th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG)
Publisher:IEEE
Document Type:conference proceeding (article)
Conference:2020 IEEE 14th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG), 08-10 July 2020, Setubal, Portugal
Language:English
Date of Publication (online):2020/08/07
Year of first Publication:2020
Number of pages:4 Seiten
First Page:425
Last Page:428
Institutes:Fakultät Elektrotechnik
IEES - Institut für Elektrische Energiesysteme
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 60 Technik
Research focus:FSP1: Energie
Publication Lists:Biechl, Helmuth
Publication reviewed:begutachtet
Release Date:2023/08/02
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