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Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental Learning
| Content Provider | MDPI |
|---|---|
| Author | Ramos, Daniel Faria, Pedro Vale, Zita Mourinho, João Correia, Regina |
| Copyright Year | 2020 |
| Description | Society’s concerns with electricity consumption have motivated researchers to improve on the way that energy consumption management is done. The reduction of energy consumption and the optimization of energy management are, therefore, two major aspects to be considered. Additionally, load forecast provides relevant information with the support of historical data allowing an enhanced energy management, allowing energy costs reduction. In this paper, the proposed consumption forecast methodology uses an Artificial Neural Network (ANN) and incremental learning to increase the forecast accuracy. The ANN is retrained daily, providing an updated forecasting model. The case study uses 16 months of data, split in 5-min periods, from a real industrial facility. The advantages of using the proposed method are illustrated with the numerical results. |
| Starting Page | 4774 |
| e-ISSN | 19961073 |
| DOI | 10.3390/en13184774 |
| Journal | Energies |
| Issue Number | 18 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-09-12 |
| Access Restriction | Open |
| Subject Keyword | Energies Industrial Engineering Artificial Neural Networks Electricity Consumption Industrial Facility Load Forecast Machine Learning |
| Content Type | Text |
| Resource Type | Article |
