10.57647/j.mjee.2024.180342

Comparison of data filtering methods effects on smartgrid load forecasting

  1. National Institute of Technology Patna, Patna, Bihar, India
Comparison of data filtering methods effects on smart grid load forecasting

Received: 2024-06-23

Revised: 2024-07-10

Accepted: 2024-07-25

Published 2024-10-11

How to Cite

Kumar, V., & Mandal, R. K. (2024). Comparison of data filtering methods effects on smartgrid load forecasting. Majlesi Journal of Electrical Engineering, 18(3), 1-10. https://doi.org/10.57647/j.mjee.2024.180342

PDF views: 50

Abstract

The integration of advanced metering technology in power systems has enabled real-time data access for every node in a smart grid. As a result, the power system can now access large volumes of data. This vast amount of data requires an alternative method of  analysis. Machine learningbased load forecasting technologies are being applied in this scenario. However, this massive data collection needs to be processed through the appropriate data pre-processing method, such as the removal of noise, outliers, and erroneous data, the detection of missing data, the normalization of widely divergent datasets, etc., to improve the effectiveness of the load  forecaster. Thus, to eliminate the various kinds of errors and outliers present in the data that was directly obtained from smart meters, this study analyses and compares the efficacy of eight distinct smoothing and filtering techniques as a novel contribution of this work. Using the processed data acquired, a neural network-based load forecasting model was developed to compare the efficacy of the  various pre-processing approaches. This study makes use of real-time data obtained from the smart meter placed at a node within the NIT Patna campus. The proposed moving average filter surpasses the other methods for filtering and smoothing the raw data by an average MAPE of 2.66, according to the load forecasting results that were obtained.

Keywords

  • Smart grid,
  • Data pre-processing,
  • Normalization,
  • Neural network,
  • Load forecasting

References

  1. A. Ahmad, X. Xiao, H. Mo, and D. Dong. “Tuning data preprocessing techniques for improved wind speed prediction.”. Energy Reports, 11:pp. 287–303, 2024. DOI: https://doi.org/10.1016/j.egyr.2023.11.056.
  2. A. Parashar, A. Parashar, W. Ding, M. Shabaz, and I. Rida. “Data preprocessing and feature selection techniques in gait recognition: A comparative study of machine learning and deep learning approaches.”. Pattern Recognition Letters, 172:pp. 65–73, 2023. DOI: https://doi.org/10.1016/j.patrec.2023.05.021.
  3. B. Boashash and Ed. “Chapter 11 - Time- Frequency Synthesis and Filtering.”. in Time Frequency Signal Analysis and Processing (Second Edition), Oxford: Academic Press, pages pp. 637– 691, 2016. DOI: https://doi.org/10.1016/B978-0-12-398499-9.00011-X.
  4. S. Rai and M. De. “Effect of Filtering in Big Data Analytics for Load Forecasting in Smart Grid in Machine Learning, Image Processing, Network Security and Data Sciences, A. Bhattacharjee, S. Kr. Borgohain, B. Soni, G. Verma, and X.-Z. Gao, Eds., in Communications in Computer and Information Science..”. Singapore: Springer, pages pp. 125–134, 2020. DOI:https://doi.org/10.1007/978-981-15-6315- 7-10.
  5. M. Aouad, H. Hajj, K. Shaban, R. A. Jabr, and W. El-Hajj. “A CNN-Sequence-to-Sequence network with attention for residential shortterm load forecasting.”. Electric Power Systems Research, 211:p. 108152, 2022. DOI: https://doi.org/10.1016/j.epsr.2022.108152.
  6. V. Chinta, G. Song, and W. Zhang. “Validation of the medium-range and sub-seasonal forecast of solar irradiance and wind speed using ECMWF.”. Energy Reports, 10:pp. 3908–3913, 2023. DOI: https://doi.org/10.1016/j.egyr.2023.10.058.
  7. A. S. F. Rocha, F. K. de O. M. V. Guerra, and M. R. B. G. Vale. “Forecasting the Performance of a Photovoltaic Solar System Installed in other Locations using Artificial Neural Networks.”. Electric Power Components and Systems, 48(1-2):pp. 201–212, 2020. DOI:https://doi.org/10.1080/15325008.2020.1736211.
  8. J. W. Taylor and R. Buizza. “Neural net- workload forecasting with weather ensemble predictions.”. IEEE Transactions on Power Systems, 17(3):pp. 626–632, 2002. DOI: https://doi.org/10.1109/TPWRS.2002.800906.
  9. S. Chapaloglou and et al. “Smart energy management algorithm for load smoothing and peak shaving based on load forecasting of an island’s power system. ”. Applied Energy, 238:pp. 627–642, 2019. DOI: https://doi.org/10.1016/j.apenergy.2019.01.102.
  10. P. Mishra, A. Biancolillo, J. M. Roger, F. Marini, and D. N. Rutledge. “New data preprocessing trends based on ensemble of multiple preprocessing techniques.”. TrAC Trends in Analytical Chemistry, 132:p. 116045, 2020. DOI: https://doi.org/10.1016/j.trac.2020.116045.
  11. E. Escobar-Avalos, M. A. Rodr´ıguez-Licea, H. Rostro- Gonza´lez, A. G. Soriano-Sa´nchez, and F. J. Pe´rez- Pinal. “A Comparison of Integrated Filtering and Prediction Methods for Smart Grids.”. Energies, 14 (7), 2021. DOI: https://doi.org/10.3390/en14071980.
  12. E. R. Davies. “CHAPTER 3 - Basic Image Filtering Operations.”. in Machine Vision (Third Edition), E. R. Davies, Ed., in Signal Processing and its Applications. Burlington: Morgan Kaufmann, pages pp. 47–101, 2005. DOI: https://doi.org/10.1016/B978-012206093-9/50006-X.
  13. E. Hussein. “Preprocessing of Measurements.”. pages pp. 97–123, 2011. DOI: https://doi.org/10.1016/B978-0-12-387777-2.00009- 4.
  14. C. Becker and U. Gather. “The Masking Breakdown Point of Multivariate Outlier Identification Rules.”. 1997. DOI: https://doi.org/10.17877/DE290R-15061.