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| Short-term Power Load Forecasting Model for Papermaking Process Based on PSO-LSSVM Algorithm |
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| DOI：10.11981/j.issn.1000-6842.2019.01.50 |
| Key Words:mathematical modeling; short-term forecasting; power load; LSSVM algorithm; PSO algorithm |
| Fund Project:国家自然科学基金重点项目（61333007）；广东省科技计划项目（2015A010104004, 2015B0101100004, 2013B010406002）；广东省自然科学基金项目（2017A030310562）；制浆造纸工程国家重点实验室开放基金（201830）。 |
| Author Name | Affiliation | | HU Yusha | State Key Lab of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong Province, 510640 | | LI Jigeng | State Key Lab of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong Province, 510640 | | HONG Mengna | State Key Lab of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong Province, 510640 | | MAN Yi* | State Key Lab of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong Province, 510640 |
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| Abstract: |
| Papermaking process consumes large amount of electricity for production.The forecast of the power load for the paper mill is conducive to the production scheduling and energy consumption reduction.A short-term power load forecasting method based on least-squares support vector machine (LSSVM) and particle swarm optimization (PSO) algorithms was proposed, which was used to forecast the power load for the next half hour in the paper mills.Compared with the industrial data collected from a paper mill, the forecasting performance showed that the mean relative error of the proposed PSO-LSSVM model was around 0.75%, which demonstrated good feasibility for the papermaking process. |
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