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Energy has a strategic role in the economic and social development of countries. In the last few decades, energy demand has been increasing exponentially across the world, and predicting energy demand has become one of the main concerns in many countries. The residential and commercial sectors constitute about 34.7% of global energy consumption. Anticipating energy demand in these sectors will help governments to supply energy sources and to develop their sustainable energy plans such as using renewable and non-renewable energy potentials for the development of a secure and environmentally friendly energy system. Modeling energy consumption in the residential and commercial sectors enables identification of the influential economic, social, and technological factors, resulting in a secure level of energy supply. In this paper, we forecast residential and commercial energy demands in Iran using three different machine learning methods, including multiple linear regression, logarithmi...
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sciepub.com SciEP
Management of energy consumption from the demand side has been inefficient and this has inadvertently affected the efforts of energy management on the supply side. This paper describes how machine learning integrated with Internet of things can enhance energy consumption management on the consumer side. Sensor nodes were designed and installed to gather data including date, time, temperature, humidity, light intensity, human presence and state of the load point switches. The sensor nodes transferred the data collected to a google firebase cloud storage which stored the data. The data collected was used in MATLAB neural network toolbox to train a machine learning algorithm that can predict the states of the receptacles, the ambient condition and the energy consumption statistics. The results show 99.7% success and 0.3% failure in the prediction of the state of the receptacle, 98.9% success and 1.1% failure in the prediction of the light state. In addition, the performance of the trained network for solving the classification and the regression problems that were involved in the prediction of the states of the switches and receptacles, the ambient condition and energy consumption statistics were shown graphically. Graphical results were also developed to show the relationship between the energy consumption, time of the day, human presence, and temperature. Conclusion was drawn on how time of the day, human presence and temperature affected the energy consumption on the consumer side.
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Horizon Research Publishing(HRPUB) Kevin Nelson
The increase in overall building energy consumption can be attributed to both socioeconomic advancement and increased urbanization. Forecasting energy consumption in buildings is critical for improving energy efficiency and sustainable development, ultimately resulting in lower energy prices and a negative environmental impact. Sustainable solutions in residential buildings aim to improve thermal comfort while lowering energy consumption. The difficulties and issues associated with residential structures may be resolved by using consumer behavior models and incorporating their inference into residential problem solutions. This article employs machine learning models that have been developed, tested, and trained to simulate energy usage in the building. Several appliances' energy data are used to evaluate the proposed predictive optimization technique. The proposed technique's results are compared to modules for prediction and optimization. To evaluate performance, regression performance measures are used. Furthermore, the results of the calculations demonstrated that the trained Random Forest Regressor model proposed in this work can accurately forecast the building's energy usage. Finally, the proposed model can be used to predict and optimize energy use in buildings that are similar to one another.
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