| 郭 晨,李雪瑞,韩照洋,付学谦.基于深度信念网络的日前电价预测[J].电力需求侧管理,2022,24(2):86-91 |
| 基于深度信念网络的日前电价预测 |
| Prediction of day-ahead electricity price based on deep belief network |
| 投稿时间：2021-11-10 修订日期：2022-01-08 |
| DOI：10. 3969 / j. issn. 1009-1831. 2022. 02 . 014 |
| 中文关键词: 电力市场 深度信念网络 小波分解 日前电价预测 |
| 英文关键词: electricity market deep belief network wavelet decomposition |
| 基金项目:国家电网有限公司科技项目（SGAH0000TKJS2100483） |
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| 中文摘要: |
| 随着我国电力体制改革的不断深入，电力市场建设取得重大进展。电价是电力市场的关键影响因素，每个参与者都基于电价进行电力交易。因此，提高电价预测的精度对于电力市场中每个参与者而言都十分重要。采用单层神经网络预测的预测精度有限。为此，根据机器学习在预测方面展示出的精准度，采用深度信念网络的方法对日前电价进行预测。在算例部分，采用美国PJM电力市场的真实数据进行仿真预测，并与其他神经网络的预测模型进行比较。算例结果表明，采用的深度信念网络模型的预测精度更高，使用深度信念网络可以为我国售电公司进行电价预测提供一种有效的方法。 |
| 英文摘要: |
| With the continuous deepening of China’s power system reform, significant progress has been made in the construction of the power market. Electricity price is a key influencing factor in the electricity market and each participant conducts electricity transactions based on electricity prices. Therefore, improving the accuracy of electricity price forecasts is very important for every participant in the electricity market. Most of the previous electricity price forecasts used single - layer neural network forecasts,and the accuracy of the forecasts was limited. To this end, according to the accuracy of machine learning in forecasting, the deep be-lief network method is used to predict the day - a - day electricity price. In the calculation example, the real data of the US PJM pow-er market is used for simulation prediction and compared with other neural network prediction models. The results of calculation examples show that the prediction accuracy of the deep belief net-work model is higher. The use of deep belief networks can providean effective method for electricity price forecasting for China’s electricity sales companies. |
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