Fuhrländer FL2500 2.5MW wind turbine dataset.

The datasetThis is a Fuhrländer FL2500 2.5MW wind turbine dataset.FormatThe dataset is stored in JSON format inside the "dataset" folder. It contains five wind turbines (80,81,82,83,84), each one with three years of data with a time interval from 2012 to 2014. The data frequency is 5 minutes reporting four indicators of each 78 sensors (a total of 312 variables). The reported values for each sensor are minimum, maximum, mean, and standard deviation for each 5-minute interval. The dataset also contains the alarms events, indicating the system and subsystem and a small description.FunctionsI have included several functions for {R,MATLAB,...} languages to providing an interface that pre-processes and manipulates the RAW data into a table-like format. The table-like format is composed of the variables at the columns and each five-minute data entry in rows.AlgorithmsIn the case of matlab code, I have included a ELM (extreme learning machines) classificator model to make some predictions as an example. The ELM model is provided by ‪Pere Marti-PuigFAQERRORSJava exception occurred: java.lang.OutOfMemoryError: Java heap spacePlease increase the matlab java heap memory to more than 4GB following the next instructions: https://es.mathworks.com/help/matlab/matlab_external/java-heap-memory-preferences.html本数据集为福兰德（Fuhrländer）FL2500型2.5MW风力发电机组数据集。 ## 数据格式 数据集存储于"dataset"文件夹内，采用JSON格式。其中包含5台风力发电机组（编号80、81、82、83、84），每台机组均带有2012年至2014年共三年的监测数据。数据采样频率为5分钟，涵盖78个传感器的4项指标，总计312个变量。每个5分钟时段内的各传感器上报值包含最小值、最大值、平均值与标准差。本数据集还包含告警事件信息，可用于指示系统及子系统状态，并附带简要说明。 ## 工具函数 本数据集附带适用于{R、MATLAB等}语言的若干函数，可提供接口将原始数据预处理并转换为类表格格式。该类表格格式以变量作为列，每条5分钟数据作为行。 ## 算法示例 针对MATLAB代码，本数据集附带极限学习机（Extreme Learning Machines, ELM）分类模型作为预测示例。该ELM模型由Pere Marti-Puig提供。 ## 常见问题与错误 发生Java异常：java.lang.OutOfMemoryError: Java堆空间不足。请按照以下说明将MATLAB的Java堆内存调整至4GB以上：https://es.mathworks.com/help/matlab/matlab_external/java-heap-memory-preferences.html