Particle Swarm Optimization-based CNN-LSTM Networks for Forecasting Energy Consumption

Tae Young Kim, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Recently, there have been many attempts to predict residential energy consumption using artificial neural networks. The optimization of these neural networks depends on the trial and error of the operator that lacks prior knowledge. They are also influenced by the initial values of the model based on the gradient algorithm and the size of the search space. In this paper, different kinds of hyperparameters are automatically determined by integrating particle swarm optimization (PSO) to CNN-LSTM network for forecasting energy consumption. Our findings reveal that the proposed optimization strategy can be used as a promising alternative prediction method for high prediction accuracy and better generalization capability. PSO achieves effective global exploration by eliminating crossover and mutation operations compared to genetic algorithms. To verify the usefulness of the proposed method, we use the household power consumption data in the UCI repository. The proposed PSO-based CNN-LSTM method explores the optimal prediction structure and achieves nearly perfect prediction performance for energy prediction. It also achieves the lowest mean square error (MSE) compared to conventional machine learning methods.

Original languageEnglish
Title of host publication2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1510-1516
Number of pages7
ISBN (Electronic)9781728121536
DOIs
Publication statusPublished - 2019 Jun
Event2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Wellington, New Zealand
Duration: 2019 Jun 102019 Jun 13

Publication series

Name2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

Conference

Conference2019 IEEE Congress on Evolutionary Computation, CEC 2019
CountryNew Zealand
CityWellington
Period19/6/1019/6/13

Fingerprint

Particle swarm optimization (PSO)
Particle Swarm Optimization
Energy Consumption
Forecasting
Energy utilization
Prediction
Optimal Prediction
Hyperparameters
Optimization
Trial and error
Gradient Algorithm
Performance Prediction
Prior Knowledge
Mean square error
Repository
Search Space
Power Consumption
Neural networks
Artificial Neural Network
Crossover

All Science Journal Classification (ASJC) codes

  • Computational Mathematics
  • Modelling and Simulation

Cite this

Kim, T. Y., & Cho, S. B. (2019). Particle Swarm Optimization-based CNN-LSTM Networks for Forecasting Energy Consumption. In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 1510-1516). [8789968] (2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2019.8789968
Kim, Tae Young ; Cho, Sung Bae. / Particle Swarm Optimization-based CNN-LSTM Networks for Forecasting Energy Consumption. 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1510-1516 (2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings).
@inproceedings{1d8a93b1e02240a18cec20df0005441f,
title = "Particle Swarm Optimization-based CNN-LSTM Networks for Forecasting Energy Consumption",
abstract = "Recently, there have been many attempts to predict residential energy consumption using artificial neural networks. The optimization of these neural networks depends on the trial and error of the operator that lacks prior knowledge. They are also influenced by the initial values of the model based on the gradient algorithm and the size of the search space. In this paper, different kinds of hyperparameters are automatically determined by integrating particle swarm optimization (PSO) to CNN-LSTM network for forecasting energy consumption. Our findings reveal that the proposed optimization strategy can be used as a promising alternative prediction method for high prediction accuracy and better generalization capability. PSO achieves effective global exploration by eliminating crossover and mutation operations compared to genetic algorithms. To verify the usefulness of the proposed method, we use the household power consumption data in the UCI repository. The proposed PSO-based CNN-LSTM method explores the optimal prediction structure and achieves nearly perfect prediction performance for energy prediction. It also achieves the lowest mean square error (MSE) compared to conventional machine learning methods.",
author = "Kim, {Tae Young} and Cho, {Sung Bae}",
year = "2019",
month = "6",
doi = "10.1109/CEC.2019.8789968",
language = "English",
series = "2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1510--1516",
booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings",
address = "United States",

}

Kim, TY & Cho, SB 2019, Particle Swarm Optimization-based CNN-LSTM Networks for Forecasting Energy Consumption. in 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings., 8789968, 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 1510-1516, 2019 IEEE Congress on Evolutionary Computation, CEC 2019, Wellington, New Zealand, 19/6/10. https://doi.org/10.1109/CEC.2019.8789968

Particle Swarm Optimization-based CNN-LSTM Networks for Forecasting Energy Consumption. / Kim, Tae Young; Cho, Sung Bae.

2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1510-1516 8789968 (2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Particle Swarm Optimization-based CNN-LSTM Networks for Forecasting Energy Consumption

AU - Kim, Tae Young

AU - Cho, Sung Bae

PY - 2019/6

Y1 - 2019/6

N2 - Recently, there have been many attempts to predict residential energy consumption using artificial neural networks. The optimization of these neural networks depends on the trial and error of the operator that lacks prior knowledge. They are also influenced by the initial values of the model based on the gradient algorithm and the size of the search space. In this paper, different kinds of hyperparameters are automatically determined by integrating particle swarm optimization (PSO) to CNN-LSTM network for forecasting energy consumption. Our findings reveal that the proposed optimization strategy can be used as a promising alternative prediction method for high prediction accuracy and better generalization capability. PSO achieves effective global exploration by eliminating crossover and mutation operations compared to genetic algorithms. To verify the usefulness of the proposed method, we use the household power consumption data in the UCI repository. The proposed PSO-based CNN-LSTM method explores the optimal prediction structure and achieves nearly perfect prediction performance for energy prediction. It also achieves the lowest mean square error (MSE) compared to conventional machine learning methods.

AB - Recently, there have been many attempts to predict residential energy consumption using artificial neural networks. The optimization of these neural networks depends on the trial and error of the operator that lacks prior knowledge. They are also influenced by the initial values of the model based on the gradient algorithm and the size of the search space. In this paper, different kinds of hyperparameters are automatically determined by integrating particle swarm optimization (PSO) to CNN-LSTM network for forecasting energy consumption. Our findings reveal that the proposed optimization strategy can be used as a promising alternative prediction method for high prediction accuracy and better generalization capability. PSO achieves effective global exploration by eliminating crossover and mutation operations compared to genetic algorithms. To verify the usefulness of the proposed method, we use the household power consumption data in the UCI repository. The proposed PSO-based CNN-LSTM method explores the optimal prediction structure and achieves nearly perfect prediction performance for energy prediction. It also achieves the lowest mean square error (MSE) compared to conventional machine learning methods.

UR - http://www.scopus.com/inward/record.url?scp=85071332279&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85071332279&partnerID=8YFLogxK

U2 - 10.1109/CEC.2019.8789968

DO - 10.1109/CEC.2019.8789968

M3 - Conference contribution

AN - SCOPUS:85071332279

T3 - 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

SP - 1510

EP - 1516

BT - 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Kim TY, Cho SB. Particle Swarm Optimization-based CNN-LSTM Networks for Forecasting Energy Consumption. In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1510-1516. 8789968. (2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings). https://doi.org/10.1109/CEC.2019.8789968