Open AccessPrediction of Future Electric Energy Consumption using Machine Learning Framework
Author(s) -
Jawad Khan,
Jyoti Rao,
Pramod S. Patil
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.c5829.029320
Subject(s) - computer science , electricity , energy consumption , energy (signal processing) , consumption (sociology) , electric power system , power demand , industrial engineering , power consumption , power (physics) , reliability engineering , engineering , social science , statistics , physics , mathematics , quantum mechanics , sociology , electrical engineering
In the last few years, the expanding energy utilization has imposed the formation of solutions for saving electricity. Of many solutions, one is generating a power saving policies which is defined as prediction of energy in smart environments. This model is built, based on the idea that the building residences are provided with smart meters to monitor energy consumption and can be managed accordingly. Recent prediction models focuses on performance of the prediction, but for developing a reliable energy system, it is required to predict the demand taking into account different scenarios. In this paper we propose a model for predicting future demand for energy according to different conditions using advanced machine learning framework. In this system we have a projector that builds proper state for a particular condition and using that defined state a future power demand is forecasted by the predictor. The proposed model generates utilization predictions for every 2 hours. Demonstrating the electricity consumption data for 5 years, the proposed system achieves a better performance.