摘要翻译：
电网并网风力发电的广泛应用要求准确可靠的风速预测，以确保稳定的电网和优质的电能。基于机器学习算法的风速预测模型由于其能够有效地从数据中捕捉变化模式，具有很强的学习能力而受到越来越多的关注。大多数已报道的基于机器学习算法的风预测模型都是针对位置的，并针对训练数据附近的数据进行了测试。在这项工作中，我们开发了基于机器学习的风速预测模型，并分析了它们在应用于未来一年来自不同交叉位置的数据时的性能。基于支持向量机(SVM)和随机森林(RF)算法的两种不同的机器学习模型已经被开发出来，并分别在相对较大的地理区域进行了测试。对一年以前不同地点和时间点的1小时预报的分析结果表明，80%的预报在均方根误差为1.5m/s范围内，95%在2.5m/s范围内，98%在3.5m/s范围内。75%的2小时预报在1.5m/s范围内，16小时预报在2.5m/s范围内，48小时预报在3.5m/s范围内。当应用于训练数据的特定位置时，这些模型可以产生长达22小时的可靠预测，其额外的优势是，这些模型在未来一年中的表现是一致的，与训练数据的提前时间无关。分析结果对风能行业在没有历史风速数据可用于模型建立和训练的地区进行风预测具有很大的前景。
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英文标题：
《Cross-location wind speed forecasting for wind energy applications using
  machine learning based models》
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作者：
Valsaraj Perumpalot, G. V. Drisya, K. Satheesh Kumar
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  The widespread utilisation of grid-integrated wind electricity necessitates accurate and reliable wind speed forecasting to ensure stable grid and quality power. Machine learning algorithm based wind speed forecasting models are getting increased attention in the literature owing to its superior ability to learn by effectively capturing the changing patterns from the data. Most of the reported wind forecasting models built on machine learning algorithms are location specific and tested against data adjacent to the training data. In this work, we develop the machine learning based wind speed forecasting models and analyse their performance when applied to data from different cross- locations up to a year ahead. Two distinct machine learning models based on Support Vector Machine (SVM) and Random Forest (RF) algorithms have been developed and tested separately for a relatively large geographical area. The results of analysis of 1-hour forecasts obtained at various cross-locations and points of time up to a year ahead show 80% of predictions within a Root Mean Square Error (RMSE) of 1.5 m/s, 95% within 2.5 m/s and 98% within an RMSEof 3.5 m/s. The 75% of 2-hour predictions are within RMSE of 1.5 m/s, 16-hour predictions within RMSE of 2.5 m / s and 48-hour predictions within RMSE of 3.5 m/s. When applied to thesame location of training data, the models generate reliable forecasts for periods up to 22 hours, with the added advantage that the models perform consistently throughout the year ahead horizon, independent of the lead time from the training data. The output of the analysis is highly promising to the wind energy industry in wind forecasting for locations where historical wind speed data are not available for model building and training.
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PDF链接：
https://arxiv.org/pdf/1808.0348