Forecasting 24-Hour Ahead Electricity Load Using Time Series Models
Forecasting electricity load is important for various purposes like planning, operation and control. Forecasts can save operating and maintenance costs, increase the reliability of power supply and delivery systems, and correct decisions for future development. This paper compares various time series methods to forecast 24 hours ahead of electricity load. The methods considered are the Holt-Winters smoothing, SARIMA Modeling, LSTM Network, Fbprophet and Tensorflow probability. The performance of each method is evaluated by using the forecasting accuracy criteria namely, the Mean Absolute Error and Root Mean Square Error. The National Renewable Energy Laboratory (NREL) residential energy consumption data are used to train the models. The results of this study show that SARIMA model is superior to the others for 24 hours ahead forecasts. Furthermore, a Bagging technique is used to make the predictions more robust. The obtained results show that by Bagging multiple time-series forecasts we can improve the robustness of the models for 24 hour ahead electricity load forecasting.
[1] K. Chen, K. Chen, Q. Wang, Z. He, J. Hu and J. He, “Short-Term
Load Forecasting With Deep Residual Networks,” IEEE Transactions
on Smart Grid, vol. 10, no. 4, pp. 3943-3952, July 2019, doi:
10.1109/TSG.2018.2844307.
[2] Cortes A and Garg A, “Impact of forecast on control methods for
customer sited battery storage”, CIRED 2019, Madrid, Spain
[3] A. A. Mamun, M. Sohel, N. Mohammad, M. S. Haque Sunny,
D. R. Dipta and E. Hossain, “A Comprehensive Review of the
Load Forecasting Techniques Using Single and Hybrid Predictive
Models,” IEEE Access, vol. 8, pp. 134911-134939, 2020, doi:
10.1109/ACCESS.2020.3010702. [4] Norizan, M., Ahmad, H.M. , Suhartono and Ismail, Z. (2011).“
Improving Short Term Load Forecasting Using Double Seasonal Arima
Model”, World Applied Sciences Journal, Vol. 15, pp. 223-231.
[5] J. Zhao, L. Li, Z. Xu, X. Wang, H. Wang and X. Shao, “Full-Scale
Distribution System Topology Identification Using Markov Random
Field,” IEEE Transactions on Smart Grid, vol. 11, no. 6, pp. 4714-
4726, Nov. 2020, doi: 10.1109/TSG.2020.2995164.
[6] J. W. Taylor and P. E. McSharry, “Short-Term Load Forecasting
Methods: An Evaluation Based on European Data,” IEEE Transactions
on Power Systems, vol. 22, no. 4, pp. 2213-2219, Nov. 2007, doi:
10.1109/TPWRS.2007.907583.
[7] J. Wang, X. Chen, F. Zhang, F. Chen and Y. Xin, “Building Load
Forecasting Using Deep Neural Network with Efficient Feature Fusion,”
Journal of Modern Power Systems and Clean Energy, vol. 9, no. 1, pp.
160-169, January 2021, doi: 10.35833/MPCE.2020.000321.
[8] Huaizhi Wang, Yangyang Liu, Bin Zhou, Canbing Li, Guangzhong
Cao, Nikolai Voropai, Evgeny Barakhtenko, “Taxonomy research of
artificial intelligence for deterministic solar power forecasting,” Energy
Conversion and Management, Volume 214, 2020, 112909.
[9] Y. Fu, Z. Li, H. Zhang, P. Xu, “Using support vector machine to pre-dict
next day electricity load of public buildings with sub-meteringdevices,”
The 9th International Symposium on Heating, Ventilationand Air
Conditioning (ISHVAC) Joint with the 3rd International Conference on
Building Energy and Environment (COBEE), 12-15 2015, Tianjin,China,
Procedia Eng. 121 (2015) 1016–1022.
[10] O. Valgaev, F. Kupzog and H. Schmeck, “Building power demand
forecasting using K-nearest neighbours model – practical application
in Smart City Demo Aspern project,” CIRED - Open Access
Proceedings Journal, vol. 2017, no. 1, pp. 1601-1604, 10 2017, doi:
10.1049/oap-cired.2017.0419.
[11] Zheng, H.; Yuan, J.; Chen, L. Short-Term Load Forecasting Using
EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature
Importance Evaluation. Energies 2017, 10, 1168.
[12] M. Mohsen, T. Faraz, S. Esmaeil, G, Noradin, and A, Oveis, “Small-
Scale building load forecast based on Hybrid forecast engine,” Neural
Processing Letters, 48(1), pp. 329-351, 2018.
[13] T. Dang-Ha, F. M. Bianchi and R. Olsson, “Local short term electricity
load forecasting: Automatic approaches,” 2017 International Joint
Conference on Neural Networks (IJCNN), Anchorage, AK, 2017, pp.
4267-4274, doi: 10.1109/IJCNN.2017.7966396.
[14] S. Papadopoulos and I. Karakatsanis, “Short-term electricity load
forecasting using time series and ensemble learning methods,” 2015
IEEE Power and Energy Conference at Illinois (PECI), Champaign, IL,
2015, pp. 1-6, doi: 10.1109/PECI.2015.7064913.
[15] A. Tokgo¨z and G. U¨ nal, “A RNN based time series approach for
forecasting turkish electricity load,” 2018 26th Signal Processing and
Communications Applications Conference (SIU), Izmir, 2018, pp. 1-4,
doi: 10.1109/SIU.2018.8404313.
[16] S. Hochreiter and J. Schmidhuber. “Long Short-Term Memory.” Neural
Computation, 9(8), 1997.
[17] Y. Hua, Z. Zhao, R. Li, X. Chen, Z. Liu and H. Zhang, “Deep Learning
with Long Short-Term Memory for Time Series Prediction,” IEEE
Communications Magazine, vol. 57, no. 6, pp. 114-119, June 2019, doi:
10.1109/MCOM.2019.1800155.
[18] Sean J Taylor and Benjamin Letham. “Forecasting at scale.” The
American Statistician, 72(1):37– 45, 2018.
[19] Software available from: http://tensorflow.org
[20] L. Breiman, “Bagging predictors,” Department of Statistics. University
of California, Berkley, California 94720, USA, Tech. Rep. 421,
September 1994.
[21] D. Dickey and F. Wayne, “Distribution of the Estimators for
Autoregressive Time Series With a Unit Root,” Journal of the
American Statistical Association 74, no. 366 (1979): 427–31,DOI:
10.1080/01621459.1979.10482531.
[22] M. Schuster and K. K. Paliwal, ”Bidirectional recurrent neural
networks,” IEEE Transactions on Signal Processing, vol. 45, no. 11,
pp. 2673-2681, Nov. 1997, doi: 10.1109/78.650093.
[23] D. Blei, A. Kucukelbir and J. McAuliffe “Variational
Inference: A Review for Statisticians,” Journal of the
American Statistical Association, 112:518, 859-877, 2017, DOI:
10.1080/01621459.2017.1285773
[24] R. Neal, “MCMC using Hamiltonian dynamics,” Handbook of Markov
Chain Monte Carlo, 2012.
[1] K. Chen, K. Chen, Q. Wang, Z. He, J. Hu and J. He, “Short-Term
Load Forecasting With Deep Residual Networks,” IEEE Transactions
on Smart Grid, vol. 10, no. 4, pp. 3943-3952, July 2019, doi:
10.1109/TSG.2018.2844307.
[2] Cortes A and Garg A, “Impact of forecast on control methods for
customer sited battery storage”, CIRED 2019, Madrid, Spain
[3] A. A. Mamun, M. Sohel, N. Mohammad, M. S. Haque Sunny,
D. R. Dipta and E. Hossain, “A Comprehensive Review of the
Load Forecasting Techniques Using Single and Hybrid Predictive
Models,” IEEE Access, vol. 8, pp. 134911-134939, 2020, doi:
10.1109/ACCESS.2020.3010702. [4] Norizan, M., Ahmad, H.M. , Suhartono and Ismail, Z. (2011).“
Improving Short Term Load Forecasting Using Double Seasonal Arima
Model”, World Applied Sciences Journal, Vol. 15, pp. 223-231.
[5] J. Zhao, L. Li, Z. Xu, X. Wang, H. Wang and X. Shao, “Full-Scale
Distribution System Topology Identification Using Markov Random
Field,” IEEE Transactions on Smart Grid, vol. 11, no. 6, pp. 4714-
4726, Nov. 2020, doi: 10.1109/TSG.2020.2995164.
[6] J. W. Taylor and P. E. McSharry, “Short-Term Load Forecasting
Methods: An Evaluation Based on European Data,” IEEE Transactions
on Power Systems, vol. 22, no. 4, pp. 2213-2219, Nov. 2007, doi:
10.1109/TPWRS.2007.907583.
[7] J. Wang, X. Chen, F. Zhang, F. Chen and Y. Xin, “Building Load
Forecasting Using Deep Neural Network with Efficient Feature Fusion,”
Journal of Modern Power Systems and Clean Energy, vol. 9, no. 1, pp.
160-169, January 2021, doi: 10.35833/MPCE.2020.000321.
[8] Huaizhi Wang, Yangyang Liu, Bin Zhou, Canbing Li, Guangzhong
Cao, Nikolai Voropai, Evgeny Barakhtenko, “Taxonomy research of
artificial intelligence for deterministic solar power forecasting,” Energy
Conversion and Management, Volume 214, 2020, 112909.
[9] Y. Fu, Z. Li, H. Zhang, P. Xu, “Using support vector machine to pre-dict
next day electricity load of public buildings with sub-meteringdevices,”
The 9th International Symposium on Heating, Ventilationand Air
Conditioning (ISHVAC) Joint with the 3rd International Conference on
Building Energy and Environment (COBEE), 12-15 2015, Tianjin,China,
Procedia Eng. 121 (2015) 1016–1022.
[10] O. Valgaev, F. Kupzog and H. Schmeck, “Building power demand
forecasting using K-nearest neighbours model – practical application
in Smart City Demo Aspern project,” CIRED - Open Access
Proceedings Journal, vol. 2017, no. 1, pp. 1601-1604, 10 2017, doi:
10.1049/oap-cired.2017.0419.
[11] Zheng, H.; Yuan, J.; Chen, L. Short-Term Load Forecasting Using
EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature
Importance Evaluation. Energies 2017, 10, 1168.
[12] M. Mohsen, T. Faraz, S. Esmaeil, G, Noradin, and A, Oveis, “Small-
Scale building load forecast based on Hybrid forecast engine,” Neural
Processing Letters, 48(1), pp. 329-351, 2018.
[13] T. Dang-Ha, F. M. Bianchi and R. Olsson, “Local short term electricity
load forecasting: Automatic approaches,” 2017 International Joint
Conference on Neural Networks (IJCNN), Anchorage, AK, 2017, pp.
4267-4274, doi: 10.1109/IJCNN.2017.7966396.
[14] S. Papadopoulos and I. Karakatsanis, “Short-term electricity load
forecasting using time series and ensemble learning methods,” 2015
IEEE Power and Energy Conference at Illinois (PECI), Champaign, IL,
2015, pp. 1-6, doi: 10.1109/PECI.2015.7064913.
[15] A. Tokgo¨z and G. U¨ nal, “A RNN based time series approach for
forecasting turkish electricity load,” 2018 26th Signal Processing and
Communications Applications Conference (SIU), Izmir, 2018, pp. 1-4,
doi: 10.1109/SIU.2018.8404313.
[16] S. Hochreiter and J. Schmidhuber. “Long Short-Term Memory.” Neural
Computation, 9(8), 1997.
[17] Y. Hua, Z. Zhao, R. Li, X. Chen, Z. Liu and H. Zhang, “Deep Learning
with Long Short-Term Memory for Time Series Prediction,” IEEE
Communications Magazine, vol. 57, no. 6, pp. 114-119, June 2019, doi:
10.1109/MCOM.2019.1800155.
[18] Sean J Taylor and Benjamin Letham. “Forecasting at scale.” The
American Statistician, 72(1):37– 45, 2018.
[19] Software available from: http://tensorflow.org
[20] L. Breiman, “Bagging predictors,” Department of Statistics. University
of California, Berkley, California 94720, USA, Tech. Rep. 421,
September 1994.
[21] D. Dickey and F. Wayne, “Distribution of the Estimators for
Autoregressive Time Series With a Unit Root,” Journal of the
American Statistical Association 74, no. 366 (1979): 427–31,DOI:
10.1080/01621459.1979.10482531.
[22] M. Schuster and K. K. Paliwal, ”Bidirectional recurrent neural
networks,” IEEE Transactions on Signal Processing, vol. 45, no. 11,
pp. 2673-2681, Nov. 1997, doi: 10.1109/78.650093.
[23] D. Blei, A. Kucukelbir and J. McAuliffe “Variational
Inference: A Review for Statisticians,” Journal of the
American Statistical Association, 112:518, 859-877, 2017, DOI:
10.1080/01621459.2017.1285773
[24] R. Neal, “MCMC using Hamiltonian dynamics,” Handbook of Markov
Chain Monte Carlo, 2012.
@article{"International Journal of Electrical, Electronic and Communication Sciences:80898", author = "Ramin Vafadary and Maryam Khanbaghi", title = "Forecasting 24-Hour Ahead Electricity Load Using Time Series Models", abstract = "Forecasting electricity load is important for various purposes like planning, operation and control. Forecasts can save operating and maintenance costs, increase the reliability of power supply and delivery systems, and correct decisions for future development. This paper compares various time series methods to forecast 24 hours ahead of electricity load. The methods considered are the Holt-Winters smoothing, SARIMA Modeling, LSTM Network, Fbprophet and Tensorflow probability. The performance of each method is evaluated by using the forecasting accuracy criteria namely, the Mean Absolute Error and Root Mean Square Error. The National Renewable Energy Laboratory (NREL) residential energy consumption data are used to train the models. The results of this study show that SARIMA model is superior to the others for 24 hours ahead forecasts. Furthermore, a Bagging technique is used to make the predictions more robust. The obtained results show that by Bagging multiple time-series forecasts we can improve the robustness of the models for 24 hour ahead electricity load forecasting.", keywords = "Bagging, Fbprophet, Holt-Winters, LSTM, Load
Forecast, SARIMA, tensorflow probability, time series.", volume = "16", number = "8", pages = "107-6", }
