A new hybrid model to predict the electrical load in five states of Australia
| Autor: | Yanyan Chen, Demeng Kong, Jinran Wu, Zhesen Cui, You-Gan Wang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Mathematical optimization
Optimization algorithm Electrical load Computer science 020209 energy Mechanical Engineering Load forecasting 02 engineering and technology Building and Construction 7. Clean energy Pollution Industrial and Manufacturing Engineering Hilbert–Huang transform Data set General Energy 020401 chemical engineering 0202 electrical engineering electronic engineering information engineering Electric power 0204 chemical engineering Electrical and Electronic Engineering Hybrid model Civil and Structural Engineering Extreme learning machine |
| Zdroj: | Energy. 166:598-609 |
| ISSN: | 0360-5442 |
| DOI: | 10.1016/j.energy.2018.10.076 |
| Popis: | Short-term electrical load forecasting is an important part in the management of electrical power because electrical load is an extreme, complex non-linear system. To obtain parameter values that provide better performances with high precision, this paper proposes a new hybrid electrical load forecasting model, which combines ensemble empirical mode decomposition, extreme learning machine, and grasshopper optimization algorithm for short-term load forecasting. The most important difference that distinguishes this electrical load forecasting model from other models is that grasshopper optimization can search suitable parameters (weight values and threshold values) of extreme learning machine, while traditional parameters are selected randomly. It is applied in Australia electrical load prediction to show its superiority and applicability. The simulation studies are carried out using a data set collected from five main states (New South Wales, Queensland, Tasmania, South Australia and Victoria) in Australia from February 1 to February 27, 2018. Compared with all considered basic models, the proposed hybrid model has the best performance in predicting electrical load. |
| Databáze: | OpenAIRE |
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