Fuzzy decision trees in predicting electricity prices

Abstract

Advancements and technologies have taken our planet to a higher level of living. In order to keep up with the times in a better and more efficient way, people tended to make major changes in their lives, whether economically, educationally or in many other fields. Because of the technology boost, electricity is nowadays considered to be one of the very most important implementations.involved. As the societies are willing to grow up, making life easier is on top of the objectives, and new innovations contributed in this fact: in deep of our concern, electricity is the main point. It is known that electricity price varies with multiple factors, but it is not obvious how the relation between the price and the factors is built. Therefore, came the idea of using the decision trees (DT) in monitoring and predicting the change in prices depending on the country itself, and on its surrounding. Based on this idea, our goal from this project is to implement an improved version of the DT, which is the fuzzy DT, in order to deal with the ability to discover how the electricity price in Denmark and inter-state power exchange, power generation, wind speed and other data are related with each other, as well as to predict the price of Denmark electricity based on these given data and on the algorithm of the fuzzy DT, and finally to compare the results in terms of accuracy under both methods.