Author(s)

Sijian Zhao ¹

Affiliation(s)

¹ School of Statistics and Mathematics, Yunnan University of Finance and Economics, Wuhua District, Kunming City, Yunnan Province, 650221, China

Corresponding Author

Sijian Zhao

ABSTRACT

This project proposes to use a combination of machine learning and time series analysis to provide in-depth analysis and forecasting of electricity consumption in a city in North Africa. The dataset used in this study contains a range of information including date, temperature, humidity, wind speed, total flow, and electricity consumption. The project proposes to reveal patterns and patterns of electricity consumption behavior through data preprocessing, normalization, and seasonal decomposition. The project proposes to use two models: Seasonal Autoregressive Integrated Sliding Average (SARIMA) and Random Forest based on feature engineering. The SARIMA method is used to analyze the seasonality and trend of the time series data, and the Random Forest method is used to study the nonlinear relationship between electricity consumption and environmental factors. On this basis, we add more information such as rolling rolling standard deviation, minimum large value, and time-delayed features to the random forest. This method greatly improves the prediction accuracy of power consumption. The experimental results show that compared with the single SARIMA model, the random forest model using j combined with the feature engineering method can better predict the load changes of the power system. The results show that the Random Forest model can capture the complexity of power consumption more effectively, especially after adding detailed feature items. At the same time, the good interpretability and flexibility of Random Forest makes the model able to better understand and predict the urban power demand, which can effectively help the power grid enterprises to realize the optimal allocation of resources and reduce energy consumption.

KEYWORDS

Power Consumption Forecasting, SARIMA, Random Forest, Time Series Analysis, Machine Learning

CITE THIS PAPER

Sijian Zhao, Urban Electricity Consumption Forecasting Based on SARIMA and Random Forest Modeling. Journal of Electrotechnology, Electrical Engineering and Management (2024) Vol. 7: 113-118. DOI: http://dx.doi.org/10.23977/jeeem.2024.070115.

REFERENCES

[1] Jiang, P., Li, R., Lu, H. et al. Modeling of electricity demand forecast for power system. Neural Comput & Applic 32, 6857–6875 (2020). 
[2] Liu, Y. Q., & Chen, X. F. (2023). Research on Power Consumption Forecasting Based on CNN+LSTM Hybrid Model. Computer and Telecommunications, (07), 0065-0070.
[3] Daneshi, Hossein, and Ali Daneshi. "Real time load forecast in power system." 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies. IEEE, 2008.
[4] Niyozov N N, Rikhsitillaev B K, Jalilova D A, et al. Forecasting of electricity consumption by industrial enterprises with a continuous nature of production based on the principal component method (PCA)[C]//E3S Web of Conferences. EDP Sciences, 2023, 384: 01031.
[5] Bilgili M, Pinar E. Gross electricity consumption forecasting using LSTM and SARIMA approaches: A case study of Türkiye [J]. Energy, 2023, 284: 128575.
[6] Zhang, Jingxin. Mathematical Modeling: Algorithms and Programming Implementations. Beijing: China Machine Press, 2022.
[7] Géron, Aurélien. Hands-On Machine Learning with Scikit-Learn and TensorFlow. Beijing: Mechanical Industry Press, 2018.
[8] https://archive.ics.uci.edu/dataset/849/power+consumption+of+tetouan+city

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