Machine Learning-Based Demand and PV Power Forecasts
| Autor: | Jneid Jneid, Franeois Bouffard, Anindita Golder, Junyuan Zhao |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
business.industry
Computer science Pv generation 020209 energy 02 engineering and technology Perceptron Machine learning computer.software_genre Renewable energy Supply and demand Support vector machine Electric power system 020401 chemical engineering 0202 electrical engineering electronic engineering information engineering Artificial intelligence 0204 chemical engineering business computer Predictive modelling Pv power |
| Zdroj: | 2019 IEEE Electrical Power and Energy Conference (EPEC). |
| DOI: | 10.1109/epec47565.2019.9074819 |
| Popis: | In recent years, predicting the performance of the power system is of high-priority to alleviate potential uncertainties. Achieving accurate load prediction at the customer and at the renewable energy resources levels will lead to more efficient balance of supply and demand. In this paper, we investigate different models including Support Vector Machines (SVM), Multi-Layer Perceptrons (MLP) and Long Short-Term Memory (LSTM). The models were used for two tasks: predicting the Load Demand and the PV generation. For the Load Demand prediction models, the dataset considered was the aggregated load demand for 40 randomly chosen homes in Austin, Texas and the weather in Austin, Texas. For the PV generation Model, the dataset considered was the PV generation of Yulara Plant in Australia and the weather conditions for the site. The MLP model gave us the best results for the Load Prediction closely followed by the LSTM and the LSTM model gave us the best result for the PV Prediction. |
| Databáze: | OpenAIRE |
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