Abstract
Energy, particularly electricity, is essential for human survival and plays a crucial role in daily life. The industry is highly tech driven with production and consumption occurring in real-time. Machine Learning and Data Science are used to address the gap between demand and supply in the electricity market. This paper examines the application of machine learning algorithms for energy consumption modeling and forecasting in smart meters. The methodology is tested on data from GNDEC Bidar, focusing on feature engineering and personalized electricity plans based on usage history.
Keywords
Energy Consumption, Forecast, Machine Learning.
Citations
IRE Journals:
Neelashetty K
"Energy Consumption Forecast of GNDEC Campus" Iconic Research And Engineering Journals Volume 7 Issue 4 2023 Page 243-247
IEEE:
Neelashetty K
"Energy Consumption Forecast of GNDEC Campus" Iconic Research And Engineering Journals, 7(4)
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