Elektrik enerjisi üretiminin tahmini: Kerbala örneği

This thesis presents a comparison and an evaluation of the performance of different regression analysis methods for estimating electricity energy production. In particular, it compares the performance of the OLS method and the Ridge regression method by analyzing the relationship among several independent variables and electricity energy production. The study employs metrics like Mean Squared Error, p-value, and Coefficient of Determination to compare the results. Additionally, the Variance Inflation Factor is used to identify any multicollinearity issues present in the data. The study is based on a random sample of (50) observations of electricity energy production from a power station. Seven independent variables (black oil, additional wages, engine oil, chemicals, operating cost, maintenance fee, and kerosene) are considered in the analysis, and their relationship with the electricity energy production (response variable) is examined. The sample was chosen to represent a real-world scenario and to provide a representative view of the factors affecting electricity energy production. The results of the study show that the Ridge regression method outperforms the OLS method in terms of MSE and the ability to handle multicollinearity issues. The use of Ridge regression allows for a more accurate estimation of electricity energy production and can be useful in making decisions related to energy production and consumption. The conclusion suggests that Ridge regression is a better option for estimating electricity energy production and can be used as a tool for managing energy resources.