GHTnet: Tri-Branch deep learning network for real-time electricity price forecasting
| Autor: | Haolin Yang, Kristen R. Schell |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Series (mathematics)
business.industry Computer science Electricity price forecasting Mechanical Engineering Deep learning Building and Construction Bidding Pollution Industrial engineering Industrial and Manufacturing Engineering Renewable energy Electric power system General Energy Benchmark (computing) Electricity market Artificial intelligence Electrical and Electronic Engineering business Civil and Structural Engineering |
| Zdroj: | Energy. 238:122052 |
| ISSN: | 0360-5442 |
| DOI: | 10.1016/j.energy.2021.122052 |
| Popis: | A highly accurate electricity price prediction model is of the utmost importance for multiple power systems tasks, such as generation dispatch and bidding. Due to the liberalization of the electricity market, as well as high renewable penetration, the properties of electricity price time series are becoming more stochastic and complex. Traditional statistical methods and machine learning algorithms cannot model such volatile market conditions with high fidelity. In this paper, we propose a data-driven deep learning network (GHTnet) to capture the temporal distribution of real-time price data. A new CNN module, based on GoogLeNet, is developed to capture the high-frequency features of this data, while inclusion of time series summary statistics is shown to improve the forecasting of volatile price spikes. The deep learning model is developed and validated on real-time price time series from 49 generators in the New York Independent System Operator (NYISO), achieving significant performance improvements over that of state-of-the-art benchmark methods, with an average 17.34% improvement in MAPE. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect