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@article{179822,
        author = {RATNOTTAR JIGAR BHANUSHANKAR and Dr. DIVYANG VYAS},
        title = {Optimization and Forecasting of Residential Energy Consumption Using Demand Side Energy Management},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {8051-8054},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179822},
        abstract = {As we enter a more technologically 
advanced era, the need for power is growing quickly. 
Nevertheless, because of an energy shortage, future 
generation's growth fails to keep up with the 
consumption. Therefore, managing loads will be 
crucial to addressing an approaching energy issue. 
Both the energy provider and customer base may 
benefit from the adoption of Demand Side Energy 
Management (DSEM). Reduced electricity bills help 
the consumer and the utility by lowering the highest 
demand periods. This study proposes using machine 
learning techniques to develop demand-side energy 
management programs for residential consumers. 
Electric load demand is predicted for the next day by 
the created load prediction models. The present study 
investigates several machine learning algorithms, 
including linear regression, random forest, decision 
tree, 
multilayer perceptron and the XGBoost 
regressor, to determine the volume of energy utilized 
in household management design. This paper uses 
R2, MAE and RMSE indicates for monitoring the 
model's performance.},
        keywords = {Demand Side Energy Management,  Machine  Learning,  Sustainable  Ecological Solution, Smart Grid},
        month = {May},
        }