基于贝叶斯深度学习的自信感知太阳辐照度预测系统

HyunYong Lee, Byung-Tak Lee
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引用次数: 5

摘要

为了光伏并网电站的稳定和成功使用,提前了解光伏电站的预期功率是非常必要的。然而,在不确定性不可避免的实际情况下,准确预测光伏输出功率是很困难的。本文提出了一种具有置信度感知的预测系统,该系统在产生点预测的同时产生点预测的置信度信息。我们的系统使用置信度信息将预测输出分为可信预测和非可信预测。然后,直接使用置信预测,用置信预测的下界代替非置信预测,以满足现有电厂的保守调度要求。通过实验,我们发现自信预测和非自信预测的最大绝对百分比误差(MAPE)分别为9.8%和21.5%。我们还表明,在95%以上的非自信预测中,下限低于实际值。结果表明,我们的方法可以很好地将预测分为可信预测和非可信预测,并产生有效的下界。
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Bayesian Deep Learning-based Confidence-aware Solar Irradiance Forecasting System
For stable and successful use of grid-connected PV (photovoltaic) plants, it is quite necessary to know the expected power from PV plants in advance. However, forecasting PV output power accurately is difficult in practical cases where uncertainties are unavoidable. In this paper, we propose a confidence-aware forecasting system that produces a point forecast together with its confidence information. Our system classifies forecast outputs into confident forecasts and non-confident forecasts using the confidence information. Then, the confident forecast is used directly and the non-confident forecast is replaced by its lower bound, which is desirable for conservative scheduling of existing power plants. Through the experiments, we show that MAPE (maximum absolute percentage error) of the confident forecasts and the non-confident forecasts are 9.8% and 21.5%, respectively. We also show that the lower bound is lower than actual value in over 95% of the non-confident forecasts. The results show that our approach is good to classify forecasts into confident forecasts and non-confident forecasts and to produce effective lower bounds.
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