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Wind Energy Resources
Published in Radian Belu, Fundamentals and Source Characteristics of Renewable Energy Systems, 2019
After the field data about the wind speed are collected and transferred to the computing environment, the next steps are to validate and process data, and generate reports. Data validation is defined as the inspection of all the collected data for completeness and reasonableness, and the elimination of erroneous values. Data validation transforms raw data into validated data. The validated data are then processed to produce the summary reports required for analysis. This step is also crucial in maintaining high rates of data completeness during the course of the monitoring program. Therefore data must be validated as soon as possible, after they are transferred. The sooner the site operator is notified of a potential measurement problem, the lower the risk of data loss. Data can be validated either manually or by using computer-based techniques. The latter is preferred to take advantage of the power and speed of computers, although some manual review will always be required. Validation software may be purchased from some data-logger vendors, created in-house using popular spreadsheet programs: e.g., Microsoft Excel, or adapted from other utility environmental monitoring projects. An advantage of using spreadsheet programs is that they can also be used to process data and generate reports. Data validation implies visual data inspection, missing data interpolation, outliers and questionable data rejection, saving data in an appropriate file format for further processing.
Big Data Optimization in Electric Power Systems: A Review
Published in Ahmed F. Zobaa, Trevor J. Bihl, Big Data Analytics in Future Power Systems, 2018
Iman Rahimi, Abdollah Ahmadi, Ahmed F. Zobaa, Ali Emrouznejad, Shady H.E. Abdel Aleem
To support a big data-based project, one first needs to analyze the data. There are specific data management tools for storing and analyzing large-scale data. Even in a simple project, there are several steps that must be performed. Figure 4.1 shows these steps that include data preparation, analysis, validation, collaboration, reporting, and access. They are briefed as follows: Data preparation is the process of collecting, cleaning, and consolidating data into one file or data table to be used in the analysis.Data analysis is the process of inspecting, cleansing, transforming, and modeling data to discover the useful information, draw conclusions, and support decision-making.Data validation is the process of ensuring that data have undergone a kind of cleansing to ensure they have acceptable quality and are correct and useful.Data collaboration means data visualization from all available different data sources while getting the data from the right people, in the right format, to be used in making effective decisions.Data reporting is the process of collecting and submitting data to authorities augmented with statistics.Data access typically refers to software and activities related to store, retrieve, or act on data housed in a database or other repository.
Tool condition monitoring framework for predictive maintenance: a case study on milling process
Published in International Journal of Production Research, 2021
E. Traini, G. Bruno, F. Lombardi
Data validation is defined as ‘an activity aimed at verifying whether the value of a data item comes from the given (finite or infinite) set of acceptable values’ (Europe 2000). The data validation of production values consists in checking that the processing parameters are in the machine's own domains. The data validation for each time series is based on two process: (1) removal of data that exceed fixed extreme values for the sensor and (2) time series outlier detection. First, if exist a such that , where is the domain of the -th sensor defined with human knowledge and physical limits of the recorder device, is removed.