Research Article
BibTex RIS Cite

Electricity Load Forecasting Using Deep Learning and Novel Hybrid Models

Year 2022, , 91 - 104, 28.02.2022
https://doi.org/10.16984/saufenbilder.982639

Abstract

Load forecasting is an essential task which is executed by electricity retail companies. By predicting the demand accurately, companies can prevent waste of resources and blackouts. Load forecasting directly affect the financial of the company and the stability of the Turkish Electricity Market. This study is conducted with an electricity retail company, and main focus of the study is to build accurate models for load. Datasets with novel features are preprocessed, then deep learning models are built in order to achieve high accuracy for these problems. Furthermore, a novel method for solving regression problems with classification approach (discretization) is developed for this study. In order to obtain more robust model, an ensemble model is developed and the success of individual models are evaluated in comparison to each other.

References

  • [1] D. I. Stern, P. J. Burke & S. B. Bruns, “The Impact of Electricity on Economic Development: A Macroeconomic Perspective”, EEG State-of-Knowledge Paper Series, 1.1, 2017.
  • [2] About the U.S. Electricity System and its Impact on the Environment, https://www.epa.gov/energy/about-us-electricity-system-and-its-impact-environment
  • [3] Energy Exchange İstanbul, Elektrik Piyasaları, https://seffaflik.epias.com.tr/transparency/
  • [4] CRO Forum, “Power Blackout Risks, Risk Management Options”, Emerging Risk Initiative – Position Paper, 2011.
  • [5] P. Szuromi, B. Jasny, D. Clery, J. Austin, & B. Hanson, “Energy for the long haul,”, 2007.
  • [6] V. Lara-Fanego, J.A. Ruiz-Arias, D. Pozo-Vázquez, F.J. Santos-Alamillos, & J. Tovar-Pescador, “Evaluation of the WRF model solar irradiance forecasts in Andalusia (southern Spain),” Solar Energy, 86(8), 2200-2217, 2012.
  • [7] IEA, “Global energy technology perspectives” International Energy Agency, OECD Publication Service, OECD, Paris, 2006a.
  • [8] B. Espinar, J.L. Aznarte, R. Girard, A.M. Moussa, & G. Kariniotakis, “Photovoltaic Forecasting: A state of the art,” In 5th European PV-Hybrid and Mini-Grid Conference (pp. Pages-250). OTTI-Ostbayerisches Technologie-Transfer-Institut, 2010.
  • [9] A. Azadeh, M. Saberi, S.F. Ghaderi, A. Gitiforouz, & V. Ebrahimipour, “Improved estimation of electricity demand function by integration of fuzzy system and data mining approach,” Energy Conversion and Man- agement, 49(8), 2165-2177, 2008.
  • [10] B. Wang, N.L. Tai, H.Q. Zhai, J. Ye, J.D. Zhu, & L.B. Qi, “A new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting,” Electric Power Systems Research, 78(10), 1679-1685, 2008.
  • [11] N. Amjady, “Short-term bus load forecasting of power systems by a new hybrid method,” IEEE Transactions on Power Systems, 22(1), 333-341, 2007.
  • [12] M. Khadem, “Application of kohonen neural network classifier to short term load forecasting,” In Panel Session on Application of Neural Networks to Short-term Load Forecasting, 1993 IEEE Winter Meeting, 1993.
  • [13] R.H. Inman, H.T. Pedro, & C.F. Coimbra, “Solar forecasting methods for renewable energy integration,” Progress in energy and combustion science, 39(6), 535-576, 2013.
  • [14] H.B. Barlow, “Unsupervised learning,” Neural computation, 1(3), 295-311, 1989.
  • [15] Y. Gala, Á. Fernández, J. Díaz, & J.R. Dorronsoro, “Hybrid machine learning forecasting of solar radiation values,” Neurocomputing, 176, 48-59, 2016.
  • [16] V. Vapnik, “The nature of statistical learning theory,” Springer science & business media, 2013.
  • [17] N. Sharma, P. Sharma, D. Irwin, & P. Shenoy, “Predicting solar generation from weather forecasts using machine learning,” In 2011 IEEE international conference on smart grid communications (SmartGridComm) (pp. 528-533). IEEE, 2011.
  • [18] J. Shi, W.J. Lee, Y. Liu, Y. Yang, & P. Wang, “Forecasting power output of photovoltaic systems based on weather classification and support vector machines.” IEEE Transactions on Industry Applications, 48(3), 1064-1069, 2012.
  • [19] L. Breiman, “Random forests,” Machine learning, 45(1), 5-32, 2001.
  • [20] J. Huertas Tato, & M. Centeno Brito, “Using Smart Persistence and Random Forests to Predict Photovoltaic Energy Production,” Energies, 12(1), 100, 2019.
  • [21] C. Voyant, C. Paoli, M. Muselli, & M.L. Nivet, “Multi-horizon solar radiation forecasting for Mediterranean locations using time series models,” Renewable and Sustainable Energy Reviews, 28, 44-52, 2013.
  • [22] R. Tibshirani, “Regression shrinkage and selection via the lasso,” Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267-288, 1996.
  • [23] J. Luo, T. Hong, & S.C. Fang, “Robust Regression Models for Load Forecasting,” IEEE Transactions on Smart Grid, 2018.
  • [24] M.Y. Ishik, T. Göze, İ. Özcan, V.Ç. Güngör, & Z. Aydın, “Short term electricity load forecasting: A case study of electric utility market in Turkey,” In 2015 3rd International Istanbul Smart Grid Congress and Fair (ICSG) (pp. 1-5). IEEE, 2015.
  • [25] W. Tan and B. Khoshnevis, “Integration of process planning and scheduling— a review,” Journal of Intelligent Manufacturing, vol. 11, no. 1, pp. 51–63, 2000.
  • [26] S. Ai, A. Chakravorty, and C. Rong. "Household Energy Consumption Prediction using Evolutionary Ensemble Neural Network." Engineering Assets and Public Infrastructures in the Age of Digitalization. Springer, Cham, 2020. 923-931.
  • [27] K. Bot, A. Ruano, and M.G. Ruano. "Forecasting electricity consumption in residential buildings for home energy management systems." International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems. Springer, Cham, 2020.
  • [28] S. Rahman, M.G. Rabiul Alam, and M. Mahbubur Rahman. "Deep Learning based Ensemble Method for Household Energy Demand Forecasting of Smart Home." 2019 22nd International Conference on Computer and Information Technology (ICCIT). IEEE, 2019.
  • [29] T. Panapongpakorn, and D. Banjerdpongchai. "Short-Term Load Forecast for Energy Management System Using Neural Networks with Mutual Information Method of Input Selection." 2019 SICE International Symposium on Control Systems (SICE ISCS). IEEE, 2019.
  • [30] S. Chan, I. Oktavianti, and V. Puspita. "A deep learning cnn and ai-tuned svm for electricity consumption forecasting: Multivariate time series data." 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). IEEE, 2019.
  • [31] M. Krishnan, Y.M. Jung, and S. Yun. "Prediction of Energy Demand in Smart Grid using Hybrid Approach." 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2020.
  • [32] H. S. Hippert, C. E. Pedreira, R. C. Souza, “Neural networks for short-term load forecasting: A review and evaluation”, IEEE Transactions on power systems, 16 (1) 44–55, 2001.
Year 2022, , 91 - 104, 28.02.2022
https://doi.org/10.16984/saufenbilder.982639

Abstract

References

  • [1] D. I. Stern, P. J. Burke & S. B. Bruns, “The Impact of Electricity on Economic Development: A Macroeconomic Perspective”, EEG State-of-Knowledge Paper Series, 1.1, 2017.
  • [2] About the U.S. Electricity System and its Impact on the Environment, https://www.epa.gov/energy/about-us-electricity-system-and-its-impact-environment
  • [3] Energy Exchange İstanbul, Elektrik Piyasaları, https://seffaflik.epias.com.tr/transparency/
  • [4] CRO Forum, “Power Blackout Risks, Risk Management Options”, Emerging Risk Initiative – Position Paper, 2011.
  • [5] P. Szuromi, B. Jasny, D. Clery, J. Austin, & B. Hanson, “Energy for the long haul,”, 2007.
  • [6] V. Lara-Fanego, J.A. Ruiz-Arias, D. Pozo-Vázquez, F.J. Santos-Alamillos, & J. Tovar-Pescador, “Evaluation of the WRF model solar irradiance forecasts in Andalusia (southern Spain),” Solar Energy, 86(8), 2200-2217, 2012.
  • [7] IEA, “Global energy technology perspectives” International Energy Agency, OECD Publication Service, OECD, Paris, 2006a.
  • [8] B. Espinar, J.L. Aznarte, R. Girard, A.M. Moussa, & G. Kariniotakis, “Photovoltaic Forecasting: A state of the art,” In 5th European PV-Hybrid and Mini-Grid Conference (pp. Pages-250). OTTI-Ostbayerisches Technologie-Transfer-Institut, 2010.
  • [9] A. Azadeh, M. Saberi, S.F. Ghaderi, A. Gitiforouz, & V. Ebrahimipour, “Improved estimation of electricity demand function by integration of fuzzy system and data mining approach,” Energy Conversion and Man- agement, 49(8), 2165-2177, 2008.
  • [10] B. Wang, N.L. Tai, H.Q. Zhai, J. Ye, J.D. Zhu, & L.B. Qi, “A new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting,” Electric Power Systems Research, 78(10), 1679-1685, 2008.
  • [11] N. Amjady, “Short-term bus load forecasting of power systems by a new hybrid method,” IEEE Transactions on Power Systems, 22(1), 333-341, 2007.
  • [12] M. Khadem, “Application of kohonen neural network classifier to short term load forecasting,” In Panel Session on Application of Neural Networks to Short-term Load Forecasting, 1993 IEEE Winter Meeting, 1993.
  • [13] R.H. Inman, H.T. Pedro, & C.F. Coimbra, “Solar forecasting methods for renewable energy integration,” Progress in energy and combustion science, 39(6), 535-576, 2013.
  • [14] H.B. Barlow, “Unsupervised learning,” Neural computation, 1(3), 295-311, 1989.
  • [15] Y. Gala, Á. Fernández, J. Díaz, & J.R. Dorronsoro, “Hybrid machine learning forecasting of solar radiation values,” Neurocomputing, 176, 48-59, 2016.
  • [16] V. Vapnik, “The nature of statistical learning theory,” Springer science & business media, 2013.
  • [17] N. Sharma, P. Sharma, D. Irwin, & P. Shenoy, “Predicting solar generation from weather forecasts using machine learning,” In 2011 IEEE international conference on smart grid communications (SmartGridComm) (pp. 528-533). IEEE, 2011.
  • [18] J. Shi, W.J. Lee, Y. Liu, Y. Yang, & P. Wang, “Forecasting power output of photovoltaic systems based on weather classification and support vector machines.” IEEE Transactions on Industry Applications, 48(3), 1064-1069, 2012.
  • [19] L. Breiman, “Random forests,” Machine learning, 45(1), 5-32, 2001.
  • [20] J. Huertas Tato, & M. Centeno Brito, “Using Smart Persistence and Random Forests to Predict Photovoltaic Energy Production,” Energies, 12(1), 100, 2019.
  • [21] C. Voyant, C. Paoli, M. Muselli, & M.L. Nivet, “Multi-horizon solar radiation forecasting for Mediterranean locations using time series models,” Renewable and Sustainable Energy Reviews, 28, 44-52, 2013.
  • [22] R. Tibshirani, “Regression shrinkage and selection via the lasso,” Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267-288, 1996.
  • [23] J. Luo, T. Hong, & S.C. Fang, “Robust Regression Models for Load Forecasting,” IEEE Transactions on Smart Grid, 2018.
  • [24] M.Y. Ishik, T. Göze, İ. Özcan, V.Ç. Güngör, & Z. Aydın, “Short term electricity load forecasting: A case study of electric utility market in Turkey,” In 2015 3rd International Istanbul Smart Grid Congress and Fair (ICSG) (pp. 1-5). IEEE, 2015.
  • [25] W. Tan and B. Khoshnevis, “Integration of process planning and scheduling— a review,” Journal of Intelligent Manufacturing, vol. 11, no. 1, pp. 51–63, 2000.
  • [26] S. Ai, A. Chakravorty, and C. Rong. "Household Energy Consumption Prediction using Evolutionary Ensemble Neural Network." Engineering Assets and Public Infrastructures in the Age of Digitalization. Springer, Cham, 2020. 923-931.
  • [27] K. Bot, A. Ruano, and M.G. Ruano. "Forecasting electricity consumption in residential buildings for home energy management systems." International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems. Springer, Cham, 2020.
  • [28] S. Rahman, M.G. Rabiul Alam, and M. Mahbubur Rahman. "Deep Learning based Ensemble Method for Household Energy Demand Forecasting of Smart Home." 2019 22nd International Conference on Computer and Information Technology (ICCIT). IEEE, 2019.
  • [29] T. Panapongpakorn, and D. Banjerdpongchai. "Short-Term Load Forecast for Energy Management System Using Neural Networks with Mutual Information Method of Input Selection." 2019 SICE International Symposium on Control Systems (SICE ISCS). IEEE, 2019.
  • [30] S. Chan, I. Oktavianti, and V. Puspita. "A deep learning cnn and ai-tuned svm for electricity consumption forecasting: Multivariate time series data." 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). IEEE, 2019.
  • [31] M. Krishnan, Y.M. Jung, and S. Yun. "Prediction of Energy Demand in Smart Grid using Hybrid Approach." 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2020.
  • [32] H. S. Hippert, C. E. Pedreira, R. C. Souza, “Neural networks for short-term load forecasting: A review and evaluation”, IEEE Transactions on power systems, 16 (1) 44–55, 2001.
There are 32 citations in total.

Details

Primary Language English
Subjects Industrial Engineering
Journal Section Research Articles
Authors

Muhammed Sütçü 0000-0002-8523-9103

Kübra Nur Şahin 0000-0001-9786-6270

Yunus Koloğlu This is me 0000-0001-6198-569X

Mevlüt Emirhan Çelikel This is me 0000-0001-9264-4345

İbrahim Tümay Gülbahar 0000-0001-9192-0782

Publication Date February 28, 2022
Submission Date August 14, 2021
Acceptance Date December 19, 2021
Published in Issue Year 2022

Cite

APA Sütçü, M., Şahin, K. N., Koloğlu, Y., Çelikel, M. E., et al. (2022). Electricity Load Forecasting Using Deep Learning and Novel Hybrid Models. Sakarya University Journal of Science, 26(1), 91-104. https://doi.org/10.16984/saufenbilder.982639
AMA Sütçü M, Şahin KN, Koloğlu Y, Çelikel ME, Gülbahar İT. Electricity Load Forecasting Using Deep Learning and Novel Hybrid Models. SAUJS. February 2022;26(1):91-104. doi:10.16984/saufenbilder.982639
Chicago Sütçü, Muhammed, Kübra Nur Şahin, Yunus Koloğlu, Mevlüt Emirhan Çelikel, and İbrahim Tümay Gülbahar. “Electricity Load Forecasting Using Deep Learning and Novel Hybrid Models”. Sakarya University Journal of Science 26, no. 1 (February 2022): 91-104. https://doi.org/10.16984/saufenbilder.982639.
EndNote Sütçü M, Şahin KN, Koloğlu Y, Çelikel ME, Gülbahar İT (February 1, 2022) Electricity Load Forecasting Using Deep Learning and Novel Hybrid Models. Sakarya University Journal of Science 26 1 91–104.
IEEE M. Sütçü, K. N. Şahin, Y. Koloğlu, M. E. Çelikel, and İ. T. Gülbahar, “Electricity Load Forecasting Using Deep Learning and Novel Hybrid Models”, SAUJS, vol. 26, no. 1, pp. 91–104, 2022, doi: 10.16984/saufenbilder.982639.
ISNAD Sütçü, Muhammed et al. “Electricity Load Forecasting Using Deep Learning and Novel Hybrid Models”. Sakarya University Journal of Science 26/1 (February 2022), 91-104. https://doi.org/10.16984/saufenbilder.982639.
JAMA Sütçü M, Şahin KN, Koloğlu Y, Çelikel ME, Gülbahar İT. Electricity Load Forecasting Using Deep Learning and Novel Hybrid Models. SAUJS. 2022;26:91–104.
MLA Sütçü, Muhammed et al. “Electricity Load Forecasting Using Deep Learning and Novel Hybrid Models”. Sakarya University Journal of Science, vol. 26, no. 1, 2022, pp. 91-104, doi:10.16984/saufenbilder.982639.
Vancouver Sütçü M, Şahin KN, Koloğlu Y, Çelikel ME, Gülbahar İT. Electricity Load Forecasting Using Deep Learning and Novel Hybrid Models. SAUJS. 2022;26(1):91-104.

30930 This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.