Predicting Electricity Consumption Using Neural Networks
| Autor: | W. G. Lopez, F. T. Romero, J. C. J. Hernandez |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Consumption (economics)
Electric power distribution Engineering General Computer Science Operations research Artificial neural network business.industry Event (computing) Machine learning computer.software_genre Backpropagation Production (economics) Electricity Artificial intelligence Electrical and Electronic Engineering Time series business computer |
| Zdroj: | IEEE Latin America Transactions. 9:1066-1072 |
| ISSN: | 1548-0992 |
| DOI: | 10.1109/tla.2011.6129704 |
| Popis: | Predict some phenomenon affects decisions of a company in the planning of resources for a greater and more efficient production. Furthermore, knowing the event will happen in the future we can take preventive measures. Therefore the main objective in this work is to make the prediction for a set of data, which correspond to the maximum monthly demand for one electric power distribution substation provided by the Commission Federal of Electricity (CFE). This prediction is made using artificial neural networks and backpropagation as the learning algorithm of the neural network, in addition we comparing these predictions with those made by the Box and Jenkins's methodology of time series. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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