• Manali Gaikwad
• Mayur Gadekar
• Sanjana Desai
• Yash Darekar

Accurately forecasting electricity consumption is very important to ensure efficient energy management and avoid resource wastage. In this study, we delve into the performance of previously trained models and discuss areas that can be improved for even more accurate forecasts. In our review of related research, we observe a predominant trend involving the use of artificial intelligence- based models, particularly Artificial Neural Networks (ANNs), in short- term electricity forecasting. These ANNs focus on accuracy metrics such as RMSE and MSE, have proven helpful at capturing intricate consumption patterns. Key input variables often include weather conditions, economic factors, historical consumption data and lifestyle indicators. This study offers a comprehensive perspective on the evolving landscape of electricity demand forecasting and introduces a novel approach to address forecasting challenges with enhanced precision.

"Household Electricity Consumption Forecasting", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 12, page no.e403-e406, December-2023, Available :http://www.jetir.org/papers/JETIR2312450.pdf

"Household Electricity Consumption Forecasting", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 12, page no. ppe403-e406, December-2023, Available at : http://www.jetir.org/papers/JETIR2312450.pdf