Descriptive statistics of Neural Network and Regression Based Results for Short Term Electricity Demand Prediction

Abstract

In the realm of data analysis and predictive modeling, both neural networks and regression techniques play pivotal roles. This study aims to provide a comparative analysis of the descriptive statistics derived from neural network and regression-based results. Utilizing a dataset representative of real-world scenarios, we explore how these two approaches perform in terms of descriptive measures such as mean squared error, coefficient of determination (R-squared), standard error, and others.

The research involves implementing both neural network and regression models on the dataset and evaluating their performance using various statistical metrics. Through a systematic examination of the descriptive statistics derived from these models, we aim to elucidate the strengths and weaknesses of each approach in capturing the underlying patterns and making accurate predictions. Additionally, we delve into the interpretability aspect, assessing the ease of understanding the results provided by neural networks compared to regression models.

Furthermore, the study investigates the impact of factors such as dataset size, complexity, and feature selection on the performance and descriptive statistics of neural networks and regression techniques. By conducting experiments across different scenarios and datasets, we aim to provide insights into the conditions under which each method excels and where potential limitations lie. The findings of this research contribute to a deeper understanding of the characteristics and capabilities of neural network and regression models in data analysis and prediction tasks. This comparative analysis serves as a valuable resource for researchers, practitioners, and stakeholders seeking to leverage these methodologies effectively in various domains, ranging from finance and economics to healthcare and beyond.