Abstract

Nowadays, with the emergence of new technologies such as smart grid and increasing the use of renewable energy in the grid, energy prediction has become more important in the electricity industry. Furthermore, with growing the integration of power generated from renewable energy sources into grids, an accurate forecasting tool for the reduction in undesirable effects of this scenario is essential. This study has developed a novel approach based on the hidden Markov model (HMM) for forecasting day-ahead solar power. The aim is to find a pattern of solar power changes at a given time in consecutive days. The proposed approach consists of two steps. In the first step, the cosine similarity is used to determine the similarity of solar power variations on consecutive days to a particular vector. In the second step, the obtained information from the first step is fed to HMM as a feature vector. These data are used for training and forecasting day-ahead solar power. After obtaining the preliminary results of the prediction, two known filters are utilized as post-processing to remove spikes and smooth the results. Finally, the performance of the proposed method is tested on real NREL data. No meteorological data (even solar radiation) are used; moreover, the model is fed only from the solar power of the past 23 days. To evaluate the proposed method, a feed-forward neural network and a simple HMM are examined with the same data and conditions. All three methods are tested with and without the post-processing. The results show that the proposed model is superior to other examined methods in terms of accuracy and computational time.

中文翻译：

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基于隐马尔可夫模型和余弦相似度的太阳能超前预报方法

摘要

如今，随着智能电网等新技术的出现以及电网中可再生能源的使用不断增加，能源预测在电力行业中变得越来越重要。此外，随着将可再生能源产生的电力集成到电网中，减少这种情况的不良影响的精确预测工具至关重要。这项研究开发了一种基于隐马尔可夫模型（HMM）的新颖方法来预测日前太阳能发电量。目的是找到连续几天在给定时间的太阳能变化模式。提议的方法包括两个步骤。在第一步中，余弦相似度用于确定连续几天的太阳能功率变化与特定矢量的相似度。第二步 从第一步获得的信息作为特征向量被馈送到HMM。这些数据用于训练和预测日间太阳能。在获得预测的初步结果之后，将两个已知的滤波器用作后处理，以消除峰值并平滑结果。最后，在真实的NREL数据上测试了该方法的性能。没有使用气象数据（甚至太阳辐射）；此外，该模型仅由过去23天的太阳能提供。为了评估所提出的方法，使用相同的数据和条件检查了前馈神经网络和简单的HMM。这三种方法都经过和不经过后处理的测试。结果表明，该模型在准确性和计算时间上均优于其他方法。