Use of Data Mining in Energy - Hande Mert

Energy plays a critical role in global economic activity—the need for energy increases in direct proportion to the rise in the expansion of the human population. Therefore decisions made on producing energy is depending on a list of costs and benefits. Renewable energy, such as solar, geothermal, biomass, hydroelectric, and wind energy, is the right choice for producing electricity, considering low-carbon investments and the impacts on the environment. It involves a complex network system composed of energy transformation, energy transportation, and energy consumption. Even though the current network system provides an excellent way for energy transportation and change, the process is still very complicated.

Energy organizations need to evaluate and track energy consumption and production to enhance their perception about their energy needs, allowing better real-time decision making for energy usages while maintaining enough energy resources for the forecasted demands. The relation between data analytics and renewable energy arises from the fact that huge data streams are increasingly needed to be observed and studied in real-time to achieve the main target of energy saving.

Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is the analysis step of the “knowledge discovery in databases” process. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.

Data mining has the power to transform enterprises, and it helps in analyzing and summarizing different elements of information. A mining process is a form in which all the data and information can be extracted for future benefits. To get a better understanding of the data mining method and its effects on renewable energy, wind energy generation can be investigated. In specific, it refers to the process of using wind to generate electricity or mechanical power.

There are a lot of studies that show the usage of data mining in energy consumption and/or energy saving. In the article, Data Mining and wind power prediction: A literature review, the authors suggest that: Wind power generated by wind turbines has a non-schedulable nature due to the stochastic nature of meteorological conditions. Hence, wind power predictions are required for a few seconds to one week ahead in turbine control, load tracking, pre-load sharing, power system management, and energy trading. To overcome problems in the predictions, many different wind power prediction models have been used to achieve in the literature. Data mining and its applications have more attention in recent years. The same study has been evaluated in consideration with their prediction accuracies and deficiencies. It is shown that adaptive neuro-fuzzy inference systems, neural networks, and multilayer perceptrons give better results in wind power predictions.

Another example of these studies is renewable energy potential in Romania using a clustering-based data mining method. In the article An assessment of the renewable energy potential using a clustering-based data mining method. Case study in Romania, the available data on installed capacity, level voltage, type of renewable technology, and geographical location the renewable energy potential for electricity generation was divided into representative zones using the K-Means clustering algorithm. For each zone, the possibility was assessed on voltage level and renewable energy generation technologies (wind, solar, hydro, biogas, biomass, and cogeneration). The zones obtained can be a useful working tool for retrofitting substations, upgrading of transmission and distribution lines, and also for redesigning them at different parameters concerning the overload. This information may enable the creation of specific programs to improve the planning and development of the electric networks in Romania.

As can be seen in the examples given, the data analytic approaches that have been applied in the field of renewable energy studies, as vast amounts of energy data are required to be analyzed to produce power on demand efficiently. Limited efforts have been investigated to apply data analytics to renewable energy data, especially wind energy. Therefore, more studies should be addressed towards data analytics for wind energy for the optimization of the wind farms’ design to predict the power generated efficiently. Good energy policy is built on useful data, and a sustained commitment to collecting adequate data is critical to meeting the world’s future energy needs. To do that, the data mining method must be understood very well by the authorities.

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