Name
Title | ||
|---|---|---|
Prediction of energy consumption time series using Neural Networks combined with exogenous series |
Abstract | ||
|---|---|---|
Artificial Neural Networks (ANNs) are widely used in various practical problems about time series. In this paper, a methodology based on exogenous series is used in combination with a Back Propagation (BP) neural network to predict time series. Exogenous series is chosen by correlation theory with endogenous series. In this way, the prediction output is obtained by not only the historical data but also the information external to historical data. Communication base station energy consumption is one important part of the total social energy consumption. So its energy consumption time series (ECTS) is used as the research data. We compare the prediction performance with the normal time delay neural network (TDNN), and the experiments show that the new method has a more precise and stable performance. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICNC.2015.7377962 | 2015 11th International Conference on Natural Computation (ICNC) |
Keywords | Field | DocType |
exogenous series,correlation theory,energy consumption time series,TDNN | Base station,Computer science,Time delay neural network,Artificial intelligence,Backpropagation,Artificial neural network,Correlation theory,Energy consumption,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bin Wu | 1 | 0 | 0.34 |
| Cui Yu | 2 | 119 | 14.87 |
| Ding Xiao | 3 | 1 | 1.37 |
| Cunyong Zhang | 4 | 0 | 0.34 |