• Kiran Basavannappagowda
• Jaswanth RS

This paper explores how statistical analysis techniques can be leveraged to enhance predictions across diverse domains. Specifically, we focus on comparing different statistical methods to develop a model that accurately predicts the power output of a combined cycle power plant. This prediction is influenced by factors such as atmospheric pressure, humidity, steam pressure, and temperature, which serve as inputs for our model. We analyze various statistical techniques, including multiple linear regression, and employ methods like forward selection to refine our model's accuracy. The objective is to create a more precise predictive model for power output. Our implementation is conducted using the Python programming language due to its extensive array of statistical tools. Utilizing Python enhances the scalability and flexibility of our model, thereby increasing its effectiveness in real-world scenarios..

Predictive analysis, power output, Multiple Linear Regression, Python

"POWER LOAD PREDICTION USING MULTIPLE LINEAR REGRESSION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.e151-e155, February-2024, Available :http://www.jetir.org/papers/JETIR2402420.pdf

"POWER LOAD PREDICTION USING MULTIPLE LINEAR REGRESSION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 2, page no. ppe151-e155, February-2024, Available at : http://www.jetir.org/papers/JETIR2402420.pdf