Name
Abstract | ||
|---|---|---|
South Africa is currently facing an on-going energy crisis that seems to persist every year. Load shedding has become one of the country's biggest challenge. This is because of energy consumption being at an all-time high and inconsistent in terms of supply. In this paper, we propose a deep learning framework (called Dense Neural Network) for the prediction of energy consumption for University buildings. The deep learning model is evaluated on an energy dataset (collected from the University of KwaZulu-Natal), to forecast the energy consumption of the buildings in the University of KwaZulu-Natal. Furthermore, we compared the performance of the proposed model with two classical algorithms (Support Vector Machine and Multiple Regression), and the deep learning model outperformed the classical algorithms. The forecasted energy consumption can be used by various University managements to assess where most of the energy is being consumed. It can provide an opportunity to devise strategies for optimal utilization of energy in Universities. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICTAS47918.2020.234004 | 2020 Conference on Information Communications Technology and Society (ICTAS) |
Keywords | DocType | ISBN |
Deep Neural Network,Energy Consumption,Energy Consumption in Building | Conference | 978-1-7281-3771-1 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simangaliso Mlangeni | 1 | 0 | 0.34 |
| Absalom E. Ezugwu | 2 | 0 | 1.01 |
| Haruna Chiroma | 3 | 0 | 0.34 |