Evaluation of Regression Algorithms in Residential Energy Consumption Prediction

Since electrical energy is generated and consumed simultaneously, predicting the energy consumption helps network operators to optimize their planning assuring stable supply of energy. Residential consumers, in particular, are subject to investigation as they share a large proportion of the total co...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:2019 3rd European Conference on Electrical Engineering and Computer Science (EECS) s. 22 - 25
Hlavní autoři: Schirmer, Pascal A., Mporas, Iosif, Potamitis, Ilyas
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.12.2019
Témata:
Energy consumption
Energy Consumption Prediction
Linear regression
Load Forecasting
Load modeling
Prediction algorithms
Predictive models
Regression Models
Task analysis
Training
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Since electrical energy is generated and consumed simultaneously, predicting the energy consumption helps network operators to optimize their planning assuring stable supply of energy. Residential consumers, in particular, are subject to investigation as they share a large proportion of the total consumed energy and have irregular components in their energy consumption signal. In this article we evaluate the performance of different regression methods in the energy prediction task. Specifically Linear Regression, Decision Trees, Deep Neural Networks, Recurrent Neural Networks, Gated Recurrent Units and Long Short Time Memory are evaluated in terms of their performance in predicting residential energy consumption. For the evaluation of the regression algorithms a large scale dataset monitoring London households for a period of several years was used. It was shown that LSTM regression model outperforms all other regression algorithms improving the accuracy of the predictor and lowering the mean absolute error up to 26.7% when compared to the baseline Linear Regression predictor.
DOI:10.1109/EECS49779.2019.00018