Version 1
: Received: 20 September 2024 / Approved: 20 September 2024 / Online: 20 September 2024 (11:28:54 CEST)
How to cite:
Park, M.-J.; Yang, H.-S. Comparative Study of Time Series Analysis Algorithms Suitable for Short-term Forecasting in Implementing Demand Response Based on AMI. Preprints2024, 2024091615. https://doi.org/10.20944/preprints202409.1615.v1
Park, M.-J.; Yang, H.-S. Comparative Study of Time Series Analysis Algorithms Suitable for Short-term Forecasting in Implementing Demand Response Based on AMI. Preprints 2024, 2024091615. https://doi.org/10.20944/preprints202409.1615.v1
Park, M.-J.; Yang, H.-S. Comparative Study of Time Series Analysis Algorithms Suitable for Short-term Forecasting in Implementing Demand Response Based on AMI. Preprints2024, 2024091615. https://doi.org/10.20944/preprints202409.1615.v1
APA Style
Park, M. J., & Yang, H. S. (2024). Comparative Study of Time Series Analysis Algorithms Suitable for Short-term Forecasting in Implementing Demand Response Based on AMI. Preprints. https://doi.org/10.20944/preprints202409.1615.v1
Chicago/Turabian Style
Park, M. and Hyo-sik Yang. 2024 "Comparative Study of Time Series Analysis Algorithms Suitable for Short-term Forecasting in Implementing Demand Response Based on AMI" Preprints. https://doi.org/10.20944/preprints202409.1615.v1
Abstract
This paper compares Demand Response algorithms suitable for Short-term Forecasting based on Advanced Metering Infrastructure (AMI). It selects ARIMA (AutoRegressive Integrated Moving Average), SARIMA (Seasonal ARIMA), LSTM (Long Short-Term Memory), and SVM (Support Vector Machine) as the primary research algorithms and delves into their theoretical foundations and functional characteristics. The theoretical background section explores each algorithm’s applicability in analyzing time-series data, recognizing nonlinear patterns, and solving optimization and classification problems. The methodology section will detail the methods of AMI data collection, the implementation of the algorithms, and the experimental setup. In the experiments and results section, the performance of each algorithm will be assessed by applying AMI data and presenting the outcomes of comparative analyses, with a focus on evaluating the predictive accuracy of the algorithms. The discussion will interpret the experimental results, analyzing the advantages and disadvantages of each algorithm and their applicability in the AMI system. It will also explore the limitations of the current research and directions for future research. The conclusion will summarize the key findings of the study and affirm how this research contributes to understanding and improving short-term forecasting and demand response systems based on AMI.
Keywords
Smart Grid, Internet of Things, SCADA, AMI, Demand Response, ARIMA, SARIMA, LSTM, SVM, Short-term Forecasting
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
