Comparative Analysis of Different Machine Learning Techniques Along with Hyper-Parameter Optimization for Prediction of Small Data Set Long Term Electricity Demand of Assam - Anurag, Sarma; Rupanjali, Nath (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Electricity is a prime and compulsory source for development of any nation. Electricity demand of Assam is increasing at an alarming rate and to compensate this loss energy is generated from many non-renewable resources, as a result Assam fails to predict and generate the future electricity demand sustainably. This is due to many factors like rapidly rising population, literacy rate, industrialization, GDP and standard of living. Therefore, predicting the future electricity demand accurately will help in better decision making in implementing energy policies and provide information regarding future energy requirements and how to generate it sustainably. In this paper an attempt has been made to assess the effectiveness of different machine learning (ML) techniques for forecasting long term electricity demand of Assam for small dataset. The data collected is a small dataset consisting of several attributes that influence the energy consumption of Assam. A small dataset has been chosen as in many cases it is challenging to see that any product or system that has been in the market for a small time need to be predicted accurately for its future demand. So, the performance of the predicted results of the ML techniques will help in understanding the challenges of predicting small dataset. And to better understand the relationship between the attributes and the response Partial Dependence Plot has been used.