Exploring the Gradient Boosting and LSTM for Power Distribution-based Time Series Analysis

Abstract

Electrical energy production is an important function in a country that has abundant demand for society. Therefore, power supply forecasting is very significant to keep the balance of power consumption and production and should not be in short supply at any cost. Thus, Forecasting and modelling time series analysis of power production becomes important in prediction. We explore the synergy between Gradient Boosting and Long Short-Term Memory (LSTM) networks in the context of time series analysis. Specifically, we investigate how XGBoost and LightGBM, well-known gradient boosting frameworks, complement LSTM, a recurrent neural network architecture. By combining these methods, we aim to uncover deeper insights and elevate predictive potential for time series forecasting. In this paper, we examine the machine learning methods of forecasting the power generation of 33-year time series data. In the conclusion, we put forward a comparative study between the outcomes attained from the use of LSTM and Gradient-boosting tree-based algorithms XGBoost and LightGBM. The results show that the XGBoost model outclasses the other models with a low error value in forecasting power generation.