Hadoop Based Data Mining and Short-Term Power Load Forecasting

In recent years, Hadoop based data mining and short-term power load forecasting have become increasingly popular. This is because the amount of data generated by the power system is increasing exponentially, making it difficult for traditional methods to handle such a large amount of data. Hadoop is a distributed computing system that allows for processing large datasets across multiple computers. It has become a popular data mining tool because it can handle a large amount of unstructured data. Short term power load forecasting is an important application of Hadoop based data mining. It includes predicting the amount of electricity that will be consumed within a given period of time, usually within the next 24 hours. This information is crucial for energy companies as it helps them plan their operations and ensure that they have sufficient supply to meet demand. In summary, Hadoop based data mining and short-term power load forecasting are important tools for efficient management and operation of energy companies. As the amount of data generated by the power system continues to increase, these tools will continue to play an important role in ensuring reliable and sustainable energy supply. In this article, a situation was considered where users must implement short-term power load forecasting technology on the Hadoop platform to estimate the maximum possible power consumption for a period of time in the future in an hour or less. This can be achieved by using HBase as data storage and Spark cluster as processing engine.