Research on the Forecasting of Electricity Demand in India State Maharashtra
DOI:
https://doi.org/10.54097/z87mq987Keywords:
Short-term power demand prediction; ARIMA Model; Maharashtra.Abstract
This study centers on forecasting the electricity demand in the Indian state of Maharashtra. In the context of rapid technological and economic development, the demand for basic raw materials and energy is on the rise. Moreover, the conflict between Russia and Ukraine has imposed supply constraints, making the prediction of electricity demand of utmost importance. Various approaches have been utilized in this field; however, existing studies often lack specificity and tend to be overly general. This research employs time series analysis, sourcing data from POSOCO. The ARIMA model is selected, and its parameters are determined through multiple rounds of testing. The results demonstrate that the ARIMA (1,0,1) model effectively fits the data, offering valuable insights. Overall, this study presents short-term forecasting and emphasizes the necessity of conducting specific and comprehensive analysis for the Intelligent Power System. Such analysis is crucial for optimizing power generation, distribution, and consumption, ensuring the stability and reliability of the power supply in Maharashtra. It also provides a foundation for decision-making in power system planning and management, enabling stakeholders to make informed choices regarding infrastructure investment and resource allocation. Additionally, the study's findings can contribute to the development of more efficient energy policies and strategies, promoting sustainable economic growth and social development in the region.
Downloads
References
[1] Huang Yonghao, Kang Chongqing, Li Hui, Xia Qing, Wang Gongli, Hu Zuohao. Modeling of Electricity Demand Curve and Its Application. Advanced Technology of Electrical Engineering and Energy, 2004.
[2] Liu Song, Chen Changqiang, Tian Shidong. Firmly Adhere to the 'Green-Based' Approach for the Transformation of the Energy Internet. China Power Enterprise Management, 2022, 35: 66-69.
[3] Smith J. Regional Variations in Electricity Demand Elasticity. Journal of Energy Economics, 2018, 45(2): 120-135.
[4] Johnson A, Brown C. The Impact of Non-Price Factors on Electricity Demand. Energy Policy, 2019, 50(5): 350-360.
[5] Davis M. Challenges in Modeling Consumer Response to Electricity Pricing. Energy Research Journal, 2020, 70(3): 450-465.
[6] Li J, Ye M, Zhao D. Passenger Flow Prediction and Risk Early Warning for Metro Cross-section based on Transportation Big Data. HSET, 2023, 78: 41-50.
[7] De Felice M, et al. Seasonal climate forecasts for medium-term electricity demand forecasting. Applied Energy, 2015, 435-444.
[8] Pegalajar M C, et al. Analysis and enhanced prediction of the spanish electricity network through big data and machine learning techniques. International Journal of Approximate Reasoning, 2021, 133(11).
[9] Semekonawo K P, Kam S. Linear regression and arima models for electricity demand forecasting in west africa. Journal of Energy Research and Reviews, 2022.
[10] Mao Yufeng. Research on power demand forecasting and seasonal adjustment model based on time series analysis. Beijing University of Technology, 2013.
[11] Chang H. Research on Short-term Passenger Flow Forecast of Metro Based on LSTM Neural Network. the master’s degree of Electronic and Information Engineering of Xijing University, 2022.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Highlights in Business, Economics and Management

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.






