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A Survey on Big Data Analytics for Load Prediction in Smart Grids

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Proceeding of the 3rd International Conference on Electronics, Biomedical Engineering, and Health Informatics

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1008))

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Abstract

Big data analytics has lately gained favor in energy management systems (EMS). EMS is in charge of overseeing, optimizing, as well as administering the electricity industry's activities. Energy utilization estimation is essential in EMS since it aids in generation planning, administration, and energy discussion. Advanced Technology, telecommunication, and automation systems are used in intelligent power grids, or "smart grids," which have become a popular trend worldwide. A challenging issue giving smart energy intelligence is predicting future network demand (energy demand). A huge number of data information is being composed through smart meters on a regular basis. Large number of analytics can aid trendy the development of intelligent energy management solutions. With this type of activity, power analysis is crucial. It is the act of gathering data since smart meters in the real period as well as since archival supplies besides smearing about the form of data investigation approach to uncover relevant relationships, tendencies, and themes. Precise predicting will permit a utility provider to strategize the resources and also to take controller actions to balance the supply and the electricity demand. The computation complexity of our investigation makes it possibly helpful for cases utilizing large-scale load prediction. Our work is capable of generating a more precise inquiry than a current prediction model, which is thought to be among the finest, according to numerous experimental results. In addition, the prediction methodologies are examined from both big data as well as traditional data perspectives.

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References

  1. Aslam S, Herodotou H, Mohsin SM, Javaid N, Ashraf N, Aslam S (2021) A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids. Renew Sustain Energy Rev 144

    Google Scholar 

  2. Rabiya K, Nadeem J (2020) A survey on hyperparameters optimization algorithms of forecasting models in smart grid. Sustain Cities Soc 61

    Google Scholar 

  3. Syed D, Zainab A, Ghrayeb A, Refaat SS, Abu-Rub H, Bouhali O (2021) Smart grid big data analytics: Survey of technologies, techniques, and applications. IEEE Access 9:59564–59585

    Article  Google Scholar 

  4. Lei C, Qu Y, Gao L, Xie G, Yu S (2020) Detecting false data attacks using machine learning techniques in smart grid: a survey. J Netw Comput Appl 170

    Google Scholar 

  5. Salkuti SR (2020) A survey of big data and machine learning. Int J Elect Comput Eng 10(1):575–580

    Google Scholar 

  6. Ancillotti E, Bruno R, Conti M (2013) The role of communication systems in smart grids: architectures, technical solutions, and research challenges. Comput Commun 36(17–18):1665–1697

    Article  Google Scholar 

  7. Da Silva PG, Dejan I, Karnouskos S (2014) The impact of smart grid prosumer grouping on forecasting accuracy and its benefits for local electricity market trading. IEEE Trans Smart Grid 5(1):402–410

    Google Scholar 

  8. Zhang H, Li Y, Shen C, Sun H, Yang Y (2015) The application of data mining in finance industry based on big data background. In: IEEE 17th international conference on high performance computing and communications. IEEE 7th international symposium on cyberspace safety and security. IEEE 12th international conference on embedded software and systems. New York, pp 1536–1539

    Google Scholar 

  9. Yang L, Zhang J-J (2017) Realistic plight of enterprise decision-making management under big data background and coping strategies. In: IEEE 2nd international conference on big data analysis. Beijing, pp 402–405

    Google Scholar 

  10. Guner S, Ozdemir A (2011) Turkish power system: from conventional past to smart future. In: 2011 2nd IEEE PES International conference and exhibition on innovative smart grid technologies. Manchester, pp 1–4

    Google Scholar 

  11. Zhang Y, Huang T, Bompard EF (2018) Big data analytics in smart grids: a review. Energy Inf 1

    Google Scholar 

  12. Baimel D, Tapuchi S, Baimel N (2016) Smart grid communication technologies-overview, research challenges and opportunities. In: International symposium on power electronics, electrical drives, automation and motion. Capri, pp 116–120

    Google Scholar 

  13. Diamantoulakis PD, Kapinas VM, Karagiannidis GK (2015) Big data analytics for dynamic energy management in smart grids. Big Data Res 2(3):94–101

    Article  Google Scholar 

  14. Zhao Z, Liu H (2007) Spectral feature selection for supervised and unsupervised learning. In: Proceedings of the 24th international conference on machine learning, pp 1151–1157

    Google Scholar 

  15. Box G, Jenkins G, Reinsel G, Ljung G (2008) Time series analysis: forecasting and control. Wiley, Hoboken, NJ, USA

    Book  MATH  Google Scholar 

  16. Zheng H, Yuan J, Chen L (2017) Short-term load forecasting using EMD-LSTM neural networks with a Xgboost algorithm for feature importance evaluation. Energies 10(8):1168

    Article  Google Scholar 

  17. Wei Z, Li X, Li X, Hu Q, Zhang H, Cui P (2017) Medium-and long-term electric power demand forecasting based on the big data of smart city. J Phys Conf Series 887, China.

    Google Scholar 

  18. Ertugrul OF (2016) Forecasting electricity load by a novel recurrent extreme learning machines approach. Int J Electr Power Energy Syst 78:429–435

    Article  Google Scholar 

  19. Moon J, Kim KH, Kim Y, Hwang E (2018) A Short term electric load forecasting scheme using 2-stage predictive analytics. In: IEEE International Conference on Big Data and Smart Computing. Shanghai, pp 219–226

    Google Scholar 

  20. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297

    Article  MATH  Google Scholar 

  21. Shayeghi H, Ghasemi A, Moradzadeh M, Nooshyar M (2015) Simultaneous day-ahead forecasting of electricity price and load in smart grids. Energy Convers Manage 95:371–384

    Article  Google Scholar 

  22. Xiao L, Wang J, Hou R, Wu J (2015) A combined model based on data pre-analysis and weight coefficients optimization for electrical load forecasting. Energy 82:524–549

    Article  Google Scholar 

  23. Dong X, Qian L, Huang L (2017) Short-term load forecasting in smart grid: a combined CNN and K-means clustering approach. In: IEEE International Conference on Big Data and Smart Computing. Jeju, p. 119–125

    Google Scholar 

  24. Katarina G, Alexandra L, Miriam A, Luke S (2016) Energy forecasting for event venues: big data and prediction accuracy. Energy Build 112:222–233

    Article  Google Scholar 

  25. Zhang P, Wu X, Wang X, Bi S (2015) Short-term load forecasting based on big data technologies. CSEE J Power Energy Syst 1(3):59–67

    Google Scholar 

  26. Arias MB Bae S (2016) Electric vehicle charging demand forecasting model based on big data technologies. Appl Energy 183:327–339

    Google Scholar 

  27. Sulaiman SM, Jeyanthy PA, Devaraj D (2016) Big data analytics of smart meter data using adaptive neuro fuzzy inference system (ANFIS). In: International conference on emerging technological trends (ICETT), pp 1–5

    Google Scholar 

  28. Grolinger K, Capretz MAM, Seewald L (2016) Energy consumption prediction with big data: balancing prediction accuracy and computational resources. In: IEEE international on congress big data, pp 157–164

    Google Scholar 

  29. Chang HH, Chiu W-Y, Hsieh T-Y (2016) Multipoint fuzzy prediction for load forecasting in green buildings. In: International conference on control, automation and systems. Gyeongju, pp 562–567

    Google Scholar 

  30. Xiao F, Wang S, Fan C (2017) Mining big building operational data for building cooling load prediction and energy efficiency improvement. In: IEEE international conference on smart computing (SMARTCOMP). Hong Kong, pp 1–3

    Google Scholar 

  31. Yu C-N, Mirowski P, Ho TK (2017) A sparse coding approach to household electricity demand forecasting in smart grids. IEEE Trans Smart Grid 8(2):738–748

    Google Scholar 

  32. Massana J, Pous C, Burgas L, Melendez J, Colomer J (2015) Short-term load forecasting in a non-residential building contrasting models and attributes. Energy Build 92:322–330

    Article  Google Scholar 

  33. Kavousi-Fard A, Samet H, Marzbani F (2014) A new hybrid modified firefly algorithm and support vector regression model for accurate short-term load forecasting. Expert Syst Appl 41(13):6047–6056

    Article  Google Scholar 

  34. Bianchi FM, De Santis E, Rizzi A, Sadeghian A (2015) Short-term electric load forecasting using echo state networks and PCA decomposition. IEEE Access 3:1931–1943

    Article  Google Scholar 

  35. Chen Y, Tan H, Song X (2017) Day-ahead forecasting of non-stationary electric power demand in commercial buildings: hybrid support vector regression based. Energy Procedia 105:2101–2106

    Article  Google Scholar 

  36. Lu Y, Zhang T, Zeng Z, Loo J (2017) An improved RBF neural network for short-term load forecast in smart grids. In: IEEE international conference on communication systems (ICCS). Shenzhen, China, pp 1–6

    Google Scholar 

  37. Li Y, Guo P, Li X (2016) Short-term load forecasting based on the analysis of user electricity behavior. Algorithms 9(4):80

    Google Scholar 

  38. Hsiao Y-H (2015) Household electricity demand forecast based on context information and user daily schedule analysis from meter data. IEEE Trans Ind Inf 11(1):33–43

    Article  Google Scholar 

  39. Quilumba FL, Lee WJ, Huang H, Wang DY, Szabados RL (2015) Using smart meter data to improve the accuracy of intraday load forecasting considering customer behavior similarities. IEEE Trans Smart Grid 6(2):911–918

    Google Scholar 

  40. Wang P, Liu B, Hong T (2016) Electric load forecasting with recency effect: a big data approach. Int J Forecast 32(3):585–597

    Article  Google Scholar 

  41. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  42. Box GEP, Jenkins GM, Reinsel GC, Ljung GM (2015) Time series analysis: forecasting and control. Wiley

    Google Scholar 

  43. Saeed A, Amirreza T, Ali C (2022) Fault detection and isolation of gas turbine using series-parallel NARX model. ISA Trans 120:205–221

    Article  Google Scholar 

  44. Xu A, Li R, Huimin C, Xu Y, Li X, Lin G, Yan Z (2022) Artificial neural network (ANN) modeling for the prediction of odor emission rates from landfill working surface. Waste Manag 138:158–171

    Google Scholar 

  45. James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning. Springer Second Edition

    Google Scholar 

  46. Aslam S, Ayub N, Farooq U, Alvi MJ, Albogamy FR, Rukh G, Haider SI, Azar AT, Bukhsh R (2021) Towards electric price and load forecasting using CNN-based ensembler in smart grid. Sustainability 13(22):12653

    Article  Google Scholar 

  47. Wang X, Liu H (2018) Soft sensor based on stacked auto-encoder deep neural network for air preheater rotor deformation prediction. Adv Eng Inform 36:112–119

    Article  Google Scholar 

  48. Aung Z, Toukhy M, Williams J, Sanchez A, Sergio H (2012) Towards accurate electricity load forecasting in smart grids. In: Fourth international conference on advances in databases, knowledge, and data applications, pp 51–57

    Google Scholar 

  49. Vapnik VN (2000) The nature of statistical learning theory. Information Science and Statistics Springer, New York

    Book  MATH  Google Scholar 

  50. Adams G, Allen PG, Morzuch BJ (1991) Probability distributions of short-term electricity peak load forecasts. Int J Forecast 7(3):283–297

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Southern Taiwan University of Science and Technology, Tainan City, 71005, Taiwan

    Seemant Tiwari

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  1. Seemant Tiwari
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Correspondence to Seemant Tiwari .

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Editors and Affiliations

  1. Department of Electromedical Engineering, Poltekkes Kemenkes Surabaya, Surabaya, Indonesia

    Triwiyanto Triwiyanto

  2. School of Electrical Engineering, Telkom University, Indonesia, Indonesia

    Achmad Rizal

  3. Faculty of Integrated Technologies, Universiti Brunei Darussalam, Gadong, Brunei Darussalam

    Wahyu Caesarendra

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Tiwari, S. (2023). A Survey on Big Data Analytics for Load Prediction in Smart Grids. In: Triwiyanto, T., Rizal, A., Caesarendra, W. (eds) Proceeding of the 3rd International Conference on Electronics, Biomedical Engineering, and Health Informatics. Lecture Notes in Electrical Engineering, vol 1008. Springer, Singapore. https://doi.org/10.1007/978-981-99-0248-4_3

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