Estimating the Wind Power by Using K-Nearest Neighbors (KNN) and Python
Journal Title: Engineering and Technology Journal - Year 2024, Vol 9, Issue 09
Abstract
In recent years the production and distribution of renewable energy has become a popular trend in power generation, and renewable energy sources such as hydro, solar, and wind energy are examples of natural and clean energy sources. Among the renewable energy sources, wind energy source is one of the trending and crucial energy sources that has outstanding electricity power generation. This research investigates an estimation model of wind power to address the real-life issues in renewable energy power generation by estimating an accurate amount of wind power production by applying the machine learning (ML) K-Nearest Neighbors (KNN) technique integrated with Python programming. To achieve the expected outcome for this research, it is expected to collect data from wind energy, wind speed forecasts, and all the relevant information that supports the research in analyzing the technique. There are various meteorological methods to evaluate the performance of wind power generation, but in this research, the methodology is to employ machine learning techniques such as KNN integrated with Python coding to achieve estimated wind energy output. Python is a software program and is diversely used for coding various algorithmic formulas to obtain results. In this research, Python is best suited for investigating the performance of the KNN technique for estimating the most accurate result for wind power generation. Here, the report presents the methods of applying the KNN technique and displaying the outcome on screen by a graph representation. The outcome of the research showed that the KNN is a more reliable technique to make an accurate prediction on wind power generation. A similar approach can be implemented in various renewable energy data to estimate an accurate energy output.
Authors and Affiliations
Abumuslim KHUJAEV , Osman YILDIRIM , Faiz BENZAIBAK,
Keywords
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- EP ID EP744561
- DOI 10.47191/etj/v9i09.13
- Views 37
- Downloads 0
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