Temporal Convolutional Network applied for Forecasting Individual Monthly Electric Energy Consumption

The task of predicting energy consumption is a problem of great interest in electric power companies. A minimal error prediction is essential for identifying inconsistencies in the monthly consumption reading process. This paper presents a methodology applied to electric consumption prediction and w...

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Bibliographic Details
Main Authors: Lemos, Victor H. B., Almeida, Joao D. S., Paiva, Anselmo C., Junior, Geraldo B., Silva, Aristofanes C., Neto, Stelmo M. B., Lima, Alan C. M., Cipriano, Carolina L. S., Fernandes, Eduardo C., Silva, Marcia I. A.
Format: Conference Proceeding
Language:English
Subjects:
Energy consumption
Forecasting
Optimization
Power systems
Statistical analysis
Task analysis
Temporal Convolutional Network
Time Series
Time series analysis
Online Access:Request full text
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Summary:The task of predicting energy consumption is a problem of great interest in electric power companies. A minimal error prediction is essential for identifying inconsistencies in the monthly consumption reading process. This paper presents a methodology applied to electric consumption prediction and was performed with and without a hyperparameter optimization strategy using a TCN network. We apply these strategies to indi-vidual electric consumption time series. The TCN approach had superior results when compared to SES, ARIMA, and Gradient Boosting. The results show that the proposed process obtained low efficiency with approximately 1% or less improvement than the use of no optimization. However, the TCN itself showed promising results being the best approach in many of our tests.
ISSN:2577-1655
DOI:10.1109/SMC42975.2020.9282960