用生成模型预测电价区间，还能捕捉波动和价格突增。
Prediction Interval Construction Method for Electricity Prices
- 用条件生成对抗网络生成电价情景，构建预测区间。
- 通过叠加情景得到概率密度，精准反映电价不确定性。
- 结合天气波动强化机制，有效应对电价异常波动。
准确预测电力市场价格在电力市场中至关重要。为反映电价的不确定性，需预测价格区间。本文提出一种新型预测区间构建方法：首先利用条件生成对抗网络生成电价情景，据此构建预测区间；随后将不同生成的情景叠加，获得概率密度分布，可更准确地刻画电价不确定性。此外，引入基于天气波动水平的强化预测机制，以应对电价飙升或剧烈波动。案例研究验证了该方法的有效性。该方法不仅能提供预测区间内各情景的概率密度，且在处理极端波动和价格突增方面具有优势。
原文摘要 · Abstract (English)
Accurate prediction of electricity prices plays an essential role in the electricity market. To reflect the uncertainty of electricity prices, price intervals are predicted. This paper proposes a novel prediction interval construction method. A conditional generative adversarial network is first presented to generate electricity price scenarios, with which the prediction intervals can be constructed. Then, different generated scenarios are stacked to obtain the probability densities, which can be applied to accurately reflect the uncertainty of electricity prices. Furthermore, a reinforced prediction mechanism based on the volatility level of weather factors is introduced to address the spikes or volatile prices. A case study is conducted to verify the effectiveness of the proposed novel prediction interval construction method. The method can also provide the probability density of each price scenario within the prediction interval and has the superiority to address the volatile prices and price spikes with a reinforced prediction mechanism.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。