Name
Abstract | ||
|---|---|---|
This paper addresses the problem of prediction accuracy of multivariate models. We propose a hybrid system to analyze the energy consumption data along with associated weather data at different time periods and address the limitations of learning techniques. Our model addresses a rather practical problem that applies to real-world scenarios where energy consumption data is influenced by multiple variables and vary according to the utility's cyber infrastructure. Such variations affect the accuracy of the model as time changes from day to day and during shoulder seasons. The proposed system combines both the long-term and the short-term learning mechanisms to achieve improved performance and accuracies. The performance and accuracy of the proposed model is evaluated experimentally using real-life data from Thunder Bay electric grid system. The results show the significance of the proposed system for practical implementations. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/Innovate-Data.2018.00014 | 2018 4th International Conference on Big Data Innovations and Applications (Innovate-Data) |
Keywords | Field | DocType |
Machine Learning,Smart Grid,Energy Consumption Prediction | Data mining,Data modeling,Thunder,Smart grid,Multivariate statistics,Cyber infrastructure,Computer science,Implementation,Artificial intelligence,Energy consumption,Hybrid system,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-5386-7794-0 | 1 | 0.40 |
References | Authors | |
2 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shailendra Singh | 1 | 15 | 2.15 |
| abdulsalam yassine | 2 | 229 | 23.42 |
| Rachid Benlamri | 3 | 135 | 23.55 |