Dual Deep Learning Networks Based Load Forecasting with Partial Real-Time Information and Its Application to System Marginal Price Prediction
| Autor: | Jung-Su Kim, Hwachang Song, Khikmafaris Yudantaka |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Control and Optimization
Computer science 020209 energy Load forecasting Energy Engineering and Power Technology smp (system marginal price) forecast 02 engineering and technology lcsh:Technology load forecast Scheduling (computing) lstm (long short-term memory) Electric power system Control theory 0202 electrical engineering electronic engineering information engineering Real-time data Electrical and Electronic Engineering Engineering (miscellaneous) SMP (system marginal price) forecast LSTM (long short-term memory) MLP (multi-layer perceptron) lcsh:T Renewable Energy Sustainability and the Environment business.industry Deep learning 020208 electrical & electronic engineering Perceptron mlp (multi-layer perceptron) Artificial intelligence business Marginal price Energy (miscellaneous) |
| Zdroj: | Energies; Volume 13; Issue 1; Pages: 148 Energies, Vol 13, Iss 1, p 148 (2019) |
| ISSN: | 1996-1073 |
| DOI: | 10.3390/en13010148 |
| Popis: | Load power forecast is one of most important tasks in power systems operation and maintenance. Enhancing its accuracy can be helpful to power systems scheduling. This paper presents how to use partial real-time temperature information in forecasting load power, which is usually done using past load power and temperature data. The partial real-time temperature information means temperature information for only part of the entire prediction time interval. To this end, a long short-term memory (LSTM) network is trained using past temperature and load power data in order to forecast load power, where forecasted load power depends on the temperature prediction implicitly. Then, in order to deal with the case where nontrivial temperature prediction errors happen, a multi-layer perceptron (MLP) network is trained using the past data describing the relation between temperature variation and load power variation. Then, the temperature is measured at the beginning of the prediction time-interval and compensated load forecast is computed by adding the output of the LSTM and that of the MLP whose input is the temperature prediction error. It is shown that the proposed compensation using the real-time temperature information indeed improves performance of load power forecast. This improved load forecast is used to predict system marginal price (SMP). The proposed method is validated using the real temperature and load power data of South Korea. |
| Databáze: | OpenAIRE |
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