skip to main content
10.1145/3640115.3640171acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiciteeConference Proceedingsconference-collections
research-article

The Investigation Focuses on the Development of a Forecasting Model for Electricity Demand, Utilizing a Fuzzy Time Series Approach

Published: 26 March 2024 Publication History

Abstract

Electricity demand forecasting is of great significance in the field of energy, which helps in rational planning and management of electricity resources. The aim of this study is to develop an electricity demand forecasting model, based on a fuzzy time series analysis approach. A large-scale dataset containing time, actual demand values, and demand forecast data provided by the Transmission System Operator (TSO) is used. The study covers the development and evaluation of univariate and multivariate models. For the univariate models, we implemented the HOFTS, WHOFTS and PWFTS models. The results show that the PWFTS model performs well in all orders and clearly outperforms the predictive performance of TSO. The model achieved an impressive accuracy of MAPE values as low as 0.87%. In terms of multivariate models, the MVFTS, Weighted MVFTS, and FIG-FTS models were applied, making full use of time partitioning and weight assignment. Although these models failed to outperform TSO in terms of performance, they demonstrated lower errors in electricity demand forecasting, showing the advantages of multivariate models in dealing with complex correlated data.

References

[1]
Bitencourt, H. V., de Souza, L. A. F., dos Santos, M. C., Silva, R., de Lima, P. C., & Guimarães, F. G. (2023). Combining embeddings and fuzzy time series for high-dimensional time series forecasting in internet of energy applications. Energy, 271, 127072.
[2]
Iftikhar, H., Bibi, N., Canas Rodrigues, P., & López-Gonzales, J. L. (2023). Multiple Novel Decomposition Techniques for Time Series Forecasting: Application to Monthly Forecasting of Electricity Consumption in Pakistan. Energies, 16(6), 2579.
[3]
Ula, M., Satriawan, I., Fhonna, R. P., & Hasibuan, A. (2023). Application of the Average Based Fuzzy Time Series Model in Predictions Seeing the Use of Travo Substations. Andalasian International Journal of Applied Science, Engineering and Technology, 3(1), 58-66.
[4]
Bitencourt, H. V., Orang, O., de Souza, L. A. F., Silva, P. C., & Guimarães, F. G. (2023). An embedding-based non-stationary fuzzy time series method for multiple output high-dimensional multivariate time series forecasting in IoT applications. Neural Computing and Applications, 35(13), 9407-9420.
[5]
Bitencourt, H. V., Orang, O., de Souza, L. A. F., Silva, P. C., & Guimarães, F. G. (2023). An embedding-based non-stationary fuzzy time series method for multiple output high-dimensional multivariate time series forecasting in IoT applications. Neural Computing and Applications, 35(13), 9407-9420.
[6]
Rahman, M. M., Shakeri, M., Khatun, F., Tiong, S. K., Alkahtani, A. A., Samsudin, N. A., ... & Hasan, M. K. (2023). A comprehensive study and performance analysis of deep neural network-based approaches in wind time-series forecasting. Journal of Reliable Intelligent Environments, 9(2), 183-200.
[7]
Behera, S., Nayak, S. C., & Kumar, A. P. (2023). A Comprehensive Survey on Higher Order Neural Networks and Evolutionary Optimization Learning Algorithms in Financial Time Series Forecasting. Archives of Computational Methods in Engineering, 1-48.
[8]
Gu, J., Zhang, W., Zhang, Y., Wang, B., Lou, W., Ye, M., & Liu, T. (2023). Research on short-term load forecasting of distribution stations based on the clustering improvement fuzzy time series algorithm. CMES-Comput. Model. Eng. Sci, 136, 2221-2236.
[9]
Rathipriya, R., Abdul Rahman, A. A., Dhamodharavadhani, S., Meero, A., & Yoganandan, G. (2023). Demand forecasting model for time-series pharmaceutical data using shallow and deep neural network model. Neural Computing and Applications, 35(2), 1945-1957.
[10]
Luzia, R., Rubio, L., & Velasquez, C. E. (2023). Sensitivity analysis for forecasting Brazilian electricity demand using artificial neural networks and hybrid models based on Autoregressive Integrated Moving Average. Energy, 274, 127365.
[11]
Behera, S., Nayak, S. C., & Kumar, A. P. (2023). A Comprehensive Survey on Higher Order Neural Networks and Evolutionary Optimization Learning Algorithms in Financial Time Series Forecasting. Archives of Computational Methods in Engineering, 1-48.
[12]
Malakouti, S. M. (2023). Utilizing time series data from 1961 to 2019 recorded around the world and machine learning to create a Global Temperature Change Prediction Model. Case Studies in Chemical and Environmental Engineering, 7, 100312.
[13]
Alghamdi, H., Maduabuchi, C., Albaker, A., Alatawi, I., Alsenani, T. R., Alsafran, A. S., ... & Alkhedher, M. (2023). A prediction model for the performance of solar photovoltaic-thermoelectric systems utilizing various semiconductors via optimal surrogate machine learning methods. Engineering Science and Technology, an International Journal, 40, 101363.
[14]
Zhuang, D., Gan, V. J., Tekler, Z. D., Chong, A., Tian, S., & Shi, X. (2023). Data-driven predictive control for smart HVAC system in IoT-integrated buildings with time-series forecasting and reinforcement learning. Applied Energy, 338, 120936.
[15]
Malakouti, S. M. (2023). Utilizing time series data from 1961 to 2019 recorded around the world and machine learning to create a Global Temperature Change Prediction Model. Case Studies in Chemical and Environmental Engineering, 7, 100312.
[16]
Li, D., Tan, Y., Zhang, Y., Miao, S., & He, S. (2023). Probabilistic forecasting method for mid-term hourly load time series based on an improved temporal fusion transformer model. International Journal of Electrical Power & Energy Systems, 146, 108743.
[17]
Klaar, A. C. R., Stefenon, S. F., Seman, L. O., Mariani, V. C., & Coelho, L. D. S. (2023). Structure Optimization of Ensemble Learning Methods and Seasonal Decomposition Approaches to Energy Price Forecasting in Latin America: A Case Study about Mexico. Energies, 16(7), 3184.
[18]
Khatri, S. A., Mirjat, N. H., Harijan, K., Uqaili, M. A., Shah, S. F., Shaikh, P. H., & Kumar, L. (2022). An overview of the current energy situation of Pakistan and the way forward towards green energy implementation. Energies, 16(1), 423.
[19]
Alghamdi, H., Maduabuchi, C., Albaker, A., Alatawi, I., Alsenani, T. R., Alsafran, A. S., ... & Alkhedher, M. (2023). A prediction model for the performance of solar photovoltaic-thermoelectric systems utilizing various semiconductors via optimal surrogate machine learning methods. Engineering Science and Technology, an International Journal, 40, 101363.
[20]
Melgar-García, L., Gutiérrez-Avilés, D., Rubio-Escudero, C., & Troncoso, A. (2023). A novel distributed forecasting method based on information fusion and incremental learning for streaming time series. Information Fusion, 95, 163-173.
[21]
Ulussever, T., Kılıç Depren, S., Kartal, M. T., & Depren, Ö. (2023). Estimation performance comparison of machine learning approaches and time series econometric models: evidence from the effect of sector-based energy consumption on CO2 emissions in the USA. Environmental Science and Pollution Research, 30(18), 52576-52592.
[22]
Abou Houran, M., Bukhari, S. M. S., Zafar, M. H., Mansoor, M., & Chen, W. (2023). COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications. Applied Energy, 349, 121638.
[23]
Ibrahim, O., Bakare, M. S., Amosa, T. I., Otuoze, A. O., Owonikoko, W. O., Ali, E. M., ... & Ogunbiyi, O. (2023). Development of fuzzy logic-based demand-side energy management system for hybrid energy sources. Energy Conversion and Management: X, 18, 100354.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
ICITEE '23: Proceedings of the 6th International Conference on Information Technologies and Electrical Engineering
November 2023
764 pages
ISBN:9798400708299
DOI:10.1145/3640115
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 March 2024

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Electricity demand forecasting
  2. Fuzzy time series
  3. Multivariate modelling
  4. Transmission system operator (TSO)
  5. Univariate modelling

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

ICITEE 2023

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 8
    Total Downloads
  • Downloads (Last 12 months)8
  • Downloads (Last 6 weeks)2
Reflects downloads up to 08 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media