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Short Term Electricity Load Forecasting using Smart Grid AI-Prediction Simulator (SGAIPS)

by Fouad Mountassir, Mohammed Bousmah
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 22
Year of Publication: 2021
Authors: Fouad Mountassir, Mohammed Bousmah

Fouad Mountassir, Mohammed Bousmah . Short Term Electricity Load Forecasting using Smart Grid AI-Prediction Simulator (SGAIPS). International Journal of Computer Applications. 183, 22 ( Aug 2021), 27-34. DOI=10.5120/ijca2021921589

@article{ 10.5120/ijca2021921589,
author = { Fouad Mountassir, Mohammed Bousmah },
title = { Short Term Electricity Load Forecasting using Smart Grid AI-Prediction Simulator (SGAIPS) },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2021 },
volume = { 183 },
number = { 22 },
month = { Aug },
year = { 2021 },
issn = { 0975-8887 },
pages = { 27-34 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2021921589 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T01:17:33.257940+05:30
%A Fouad Mountassir
%A Mohammed Bousmah
%T Short Term Electricity Load Forecasting using Smart Grid AI-Prediction Simulator (SGAIPS)
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 22
%P 27-34
%D 2021
%I Foundation of Computer Science (FCS), NY, USA

Electricity Demand Forecasting techniques with Artificial Intelligence based on Machine Learning and Deep Learning Models have been widely used in the past 20 years. These techniques play an important role in the construction of next generation of Smart Grid. However, applied machine learning in this real world presents challenges, it requires resources (Datasets from Smart Meters and other IoT-connected devices), skills (Machine Learning Algorithms, python or other programming languages) and knowledge (conventional load forecasting methods, intelligent load forecasting methods). For this purpose, we have developed, a new Smart Grid Artificial Intelligence Prediction Simulator called SGAIPS, that can provide a systematic short term electricity load forecasting based on Smart meters and Machine Learning algorithms. The starting point for using SGAIPS is the construction of dataset by loading or generating smart meters’ data. The next step is to create, train and test the machine learning models. And the final step is to predict the energy consumption. SGAIPS is a zero code Simulator for Smart Grid, that aims to simplify the creation and implementation of virtual smart meters, machine learning models and intelligent forecasting methods.

  1. F. Mountassir, R. Mali, A. Lmouatassime, M. Bousmah, “Machine Learning and IoT for Smart Grid” In Proceedings of the 5th International Conference on SMart City Applications (SCA), Safranbolu, Turkey, 7–9 October 2020.
  2. Lemuel Clark P. Velasco, Daisy Lou L. Polestico, Gary Paolo O. Macasieb, Michael Bryan V. Reyes and Felicisimo B. Vasquez Jr , “Load Forecasting using Autoregressive Integrated Moving Average and Artificial Neural Network ” International Journal of Advanced Computer Science and Applications(ijacsa), 9(7), 2018.
  3. A. Hammad, B. Jereb, B. Rosi, D. Dragan., “Methods and Models for Electric Load Forecasting: A Comprehensive Review,” Logistics & Sustainable Transport, vol.11, no.1, 2020, pp.51-76.
  4. E. Almeshaiei and H. Soltan, "A Methodology for Electric Power Load Forecasting," Alexandria Engineering Journal, vol. 50, no. 2011, pp. 137–144, 2011.
  5. J. Zhang, "Research on Power Load Forecasting Based on the Improved Elman Neural Network," The Italian Association of Chemical Engineering (AIDIC), vol. 51, no. 2016, pp. 589-594, 2016.
  6. Ü. B. Filik, Ö. N. Gerek, and M. Kurban, "A Novel Modeling Approach for Hourly Forecasting of Long-Term Electric Energy Demand," Energy Conversion and Management, vol. 52, no. 2011, pp. 199–211, 2011.
  7. R. Gordillo-Orquera, L. M. Lopez-Ramos, S. Muñoz-Romero, P. Iglesias-Casarrubios, D. Arcos-Avilés, A. G. Marques, and J. L. Rojo-Álvarez, "Analyzing and Forecasting Electrical Load Consumption in Healthcare Buildings," Energies, vol. 11, no. 493, 2018.
  8. G. Nalcaci, A. Özmen, and G. W. Weber, "Long-term Load Forecasting: Models Based on MARS, ANN and LR methods," Central European Journal of Operations Research (CEJOR), Springer-Verlag GmbH Germany, vol. 27, no. 2019, pp. 1033–1049, 2018.
  9. L. Friedrich and A. Afshari, "Short-Term Forecasting of the Abu Dhabi Electricity Load Using Multiple Weather Variables," presented at the 7th International Conference on Applied Energy (ICAE), 2015.
  10. R. Wanga, J. Wangb, and Y. Xu, "A Novel Combined Model Based on Bybrid Optimization Algorithm for Electrical Load Forecasting," Applied Soft Computing Journal, vol. 82, no. 2019, p. 105548, 2019.
  11. N. Abu-Shikhah and F. Elkarmi, "Medium-Term Electric Load Forecasting Using Singular Value Decomposition," Energy Conversion and Management, vol. 36, no. 7, pp. 4259-4271, 2011.
  12. Lei Zhang, Linghui Yang , Chengyu Gu, Da Li, “LSTM-based Short-term Electrical Load Forecasting and Anomaly Correction,” E3S Web of Conferences182,01004(2020),
  13. G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, " Time series analysis: Forecasting and control ," 4th ed. ed. (Wiley series in probability and statistics). Oxford: Wiley, 2008.
  14. P. S. P. Cowpertwait and A. V. Metcalfe, “Introductory Time Series with R”. Springer New York, 2009.
  15. A. J. Wood, B. F. Wollenberg, and G. B. Sheblé, “Power Generation, Operation, and Control.” Wiley, 2013.
  16. Fu Y, Li Z, Zhang H, et al. “Using support vector machine to predict next day electricity load of public buildings with sub-metering devices.” Procedia Engineering 121: 1016-1022 (2015).
  17. J. G. Jetcheva, M. Majidpour, W. P. Chen, “Neural network model ensembles for building-level electricity load forecasts,” Energy and Buildings 84, 214-223 (2014).
  18. J. Ku, R. Goomer, A. K. Singh, “Long Short Term Memory Recurrent Neural Network (LSTM-RNN) Based Workload Forecasting Model For Cloud Datacenters,” Procedia Computer Science 125, 676- 682 (2018).
  19. Y. H. Zhang, C. M. Qiu, X. He, et al., “A Short-Term Load Forecasting Based on LSTM Neural Network,” Electric Power Information & Communication Technology, (2017).
  20. Delivery, Kee Yuan Ngiam, Ing Wei Khor, “Big data and machine learning algorithms for health-care,” Lancet Oncol 2019; 20: e262–73.
  21. Jafar Alzubi, Nayyar Anand, Kumar Akshi, “Machine Learning from Theory to Algorithms: An Overview,” 2018 J. Phys.: Conf. Ser. 1142 012012.
  22. Bengio Y., Courville A., Vincent P.,"Representation Learning: A Review and New Perspectives,". IEEE Transactions on Pattern Analysis and Machine Intelligence (2013). 35 (8): 1798–1828. arXiv:1206.5538. doi:10.1109/tpami.2013.50. PMID 23787338. S2CID 393948.
  23. Schmidhuber J., "Deep Learning in Neural Networks: An Overview". Neural Networks. 61: 85–117(2015). arXiv:1404.7828. doi:10.1016/j.neunet.2014.09.003. PMID 25462637. S2CID 11715509.
  24. Bengio Yoshua, LeCun, Yann, Hinton Geoffrey (2015). "Deep Learning". Nature. 521 (7553): 436–444. Bibcode:2015 Natur.521..436L. doi:10.1038/nature14539. PMID 26017442. S2CID 3074096.
  25. Antonopoulos, I, Robu, V, Couraud, B, Kirli, D, Norbu, S, Kiprakis, A, Flynn, D, Elizondo-González, S & Wattam, S 2020, “Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review”, Renewable and Sustainable Energy Reviews, vol. 130, 109899.
  26. Yuntian Chen, Dongxiao Zhang, “Theory-guided deep-learning for electrical load forecasting (TgDLF) via ensemble long short-term memory”, Advances in Applied Energy, Volume 1, 2021, 100004.
  27. Bedi J, Toshniwal D. “Deep learning framework to forecast electricity demand”, Appl Energy 2019;238:1312–26.
  28. Kwon BS, Park RJ, Song KB. “Short-term load forecasting based on deep neural networks using LSTM layer”, J Electr Eng Technol 2020;15:1501–9. doi:10.1007/s42835-020-00424-7.
  29. Sun G, Jiang C, Wang X, Yang X, “Short-term building load forecast based on a data- mining feature selection and LSTM-RNN method”, IEEE Trans Electr Electron Eng 2020;15(7):1002–10. doi:10.1002/tee.23144.
  30. Nespoli A, Ogliari E, Pretto S, Gavazzeni M, Vigani S, Paccanelli F, “Data quality analysis in day-ahead load forecast by means of LSTM”, Proceedings of the IEEE international conference on environment and electrical engineering and IEEE industrial and commercial power systems Europe (EEEIC/I&CPS Europe). IEEE; 2020.
  31. Hossain MS, Mahmood H, “Short-term load forecasting using an LSTM neural network”, Proceedings of the IEEE power and energy conference at Illinois (PECI). IEEE; 2020.
  32. Lampropoulos I, Vanalme GM, Kling WL, “A methodology for modeling the behavior of electricity prosumers within the smart grid”, 2010 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe). IEEE, Gothenberg, pp 1–8
  33. Lopes AJ, Lezama R, Pineda R, “Model based systems engineering for smart grids as systems of systems”, Procedia Comput Sci 6:441–450, 2011.
  34. Andrén F, Stifter M, Strasser T, “Towards a semantic driven framework for smart grid applications: Model-driven development using cim”, iec 61850 and iec 61499. Informatik-Spektrum 36(1):58–68, 2013.
  35. Godfrey T, Mullen S, Griffith DW, Golmie N, Dugan RC, Rodine C “Modeling smart grid applications with co-simulation”,First IEEE International Conference on Smart Grid Communications. IEEE, Gaithersburg, pp 291–296, {2010).
  36. Yang C-H, Zhabelova G, Yang C-W, Vyatkin V, “Cosimulation environment for event-driven distributed controls of smart grid”, IEEE Trans Ind Inf 9(3):1423–1435, (2013).
  37. Palensky P, Widl E, Elsheikh A, “Simulating cyber-physical energy systems: Challenges, tools and methods”, IEEE TransSyst Man Cybern Syst 44(3):318–326, (2013).
  38. Christoph Binder, Michael Fischinger, Lukas Altenhuber, Dieter Draxler, Goran Lastro and Christian Neureiter, “Enabling architecture based Co-Simulation of complex Smart Grid applications”, Energy Informatics (2019), 2 (Suppl1): 20,
  39. Schütte S, Scherfke S, Tröschel M, “Mosaik: A framework for modular simulation of active components in smart grids”, Smart Grid Modeling and Simulation (SGMS), 2011 IEEE First International Workshop On. IEEE, Brussels. pp 55–60.
  40. The HOMER Pro® microgrid.
  41. The Load Profile Generator.
  42. Kaggle: The world's largest data science community
  43. Rob Mulla, “Over 10 years of hourly energy consumption data from PJM in Megawatts”, Kaggle, 2018.
Index Terms

Computer Science
Information Sciences


Short-Term Load Forecasting Artificial Intelligence Smart Meters Smart Grid Prediction Simulator.