Copyright © 2026 Authors retain the copyright of this article. This article 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.

@article{166544,
        author = {Pooja Nitin Chaudhari and Vaishali Bhaskar Mistari and Paresh Pradip Gajare and Dr. D. D. Patil},
        title = {Electricity Demand using Time Series Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {2},
        pages = {1053-1056},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=166544},
        abstract = {Throughout the years, there has been a constant rise in the need for power. A strong predictive model is necessary to comprehend future consumption. The planning of power production and the determination of resources required to run the plants, such as fuels, depend heavily on the forecasting of energy demand. It also aids in the planning of future electricity requirements, which leads to the establishment of additional networks and plants. There are about 10 million people living in the city of London, which also contains 3.6 million homes, numerous businesses, and an industrial sector. Every year, energy bills in London's homes and offices cost upwards of £7.9 billion; this money does not stay in the city's economy.},
        keywords = {ARIMA, SARIMAX, LSTM, MLP, EXOG.},
        month = {July},
        }