Enhancement of a Short-Term Forecasting Method Based on Clustering and kNN: Application to an Industrial Facility Powered by a Cogenerator
| Autor: | Marco Noro, Giulio Vialetto |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
polygeneration
Control and Optimization Computer science 020209 energy Big data Energy Engineering and Power Technology forecasting 02 engineering and technology lcsh:Technology Underdevelopment big data data analytics big data forecasting energy polygeneration clustering kNN pattern recognition 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering data analytics Cluster analysis Wood industry Engineering (miscellaneous) lcsh:T Renewable Energy Sustainability and the Environment business.industry pattern recognition kNN Information technology 021001 nanoscience & nanotechnology Industrial engineering Computer data storage The Internet Electricity 0210 nano-technology business energy clustering Energy (miscellaneous) |
| Zdroj: | Energies; Volume 12; Issue 23; Pages: 4407 Energies, Vol 12, Iss 23, p 4407 (2019) |
| ISSN: | 1996-1073 |
| DOI: | 10.3390/en12234407 |
| Popis: | In recent years, collecting data is becoming easier and cheaper thanks to many improvements in information technology (IT). The connection of sensors to the internet is becoming cheaper and easier (for example, the internet of things, IOT), the cost of data storage and data processing is decreasing, meanwhile artificial intelligence and machine learning methods are under development and/or being introduced to create values using data. In this paper, a clustering approach for the short-term forecasting of energy demand in industrial facilities is presented. A model based on clustering and k-nearest neighbors (kNN) is proposed to analyze and forecast data, and the novelties on model parameters definition to improve its accuracy are presented. The model is then applied to an industrial facility (wood industry) with contemporaneous demand of electricity and heat. An analysis of the parameters and the results of the model is performed, showing a forecast of electricity demand with an error of 3%. |
| Databáze: | OpenAIRE |
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