Abstract
In order to analyze the way residential sector energy is consumed putting into consideration certain factors, this study predicted the United States residential sector energy consumption from 1984 to 2010. The factors having impact on the way energy is consumed were assessed using the connection weight approach while the energy is being predicted. Artificial Neural Network was successfully applied in the prediction with a correlation coefficient of 0.97903. It was observed that the median household income was the most important factor in the consumption of residential sector energy consumption with a percentage of 93% followed by household size and cost of residential natural gas with 90% and 56.5% respectively while resident population was the least important factor followed by cost of residential heating oil, gross domestic product and cost of electricity in percentages of -76%, -51%, -30.5%, and 18% respectively.
| Original language | English |
|---|---|
| Pages (from-to) | 31-38 |
| Number of pages | 8 |
| Journal | Proceedings of the International Conference on Industrial Engineering and Operations Management |
| Volume | 2017 |
| Issue number | JUL |
| Publication status | Published - 2017 |
| Event | European International Conference on Industrial Engineering and Operations Management.IEOM 2017 - Duration: 24 Jul 2017 → 25 Jul 2017 |
Keywords
- Analyze
- Artificial neural network
- Connection weight
- Predict
- Residential sector energy
ASJC Scopus subject areas
- Strategy and Management
- Management Science and Operations Research
- Control and Systems Engineering
- Industrial and Manufacturing Engineering