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Electricity load prediction plays a vital role in energy management. The necessity of combining scattered power generation channels of sustainable energy and other substitute resources into distribution grid networks is increasing because of the 4.1 percent rise in world electricity since 2021. The utilization of probability power generators and energy storage is maximized by short-term load prediction methods, which make it possible to anticipate potential energy usage. To facilitate the growth of energy systems in quite a sustainable way, urban energy planners utilize a range of methods and technological advances, including simulation, probability optimization, comprehensive improvement, and modelling techniques for predicting power requirements. Predicting energy consumption at granular time scales (e.g., hourly or sub-hourly intervals) is common practice for short-term energy load prediction. For similar time-series forecasting challenges, the authors have decided to use transfer learning (TL) with light gradient-boosting machine (LightGBM).1 This model can benefit from models trained on larger data sets through TL, which might boost prediction accuracy. Energy prediction and effective resource utilization largely support the accurate transition of energy systems. In this paper, the authors propose a hybrid model for short-term energy load prediction. Based on TL to optimize LightGBM (OLGBM) for smart grids, The proposed model first applies data pre-processing using an abnormal supplement strategy with an immediate deviation selection technique. This phase eliminates the missing values and extracts the required features.The second phase uses a TL-OLGBM with hyperparameters. The TL method helps to learn the dynamic time scale and complex data patterns, and the hyperparameters are tuned via the Bayesian optimization technique, which addresses the challenge of short-term load forecasting (STLF) and increases forecasting accuracy. Finally, an optimized LightGBM model is applied, combining the time and energy features to predict effective energy forecasting. The proposed model gained a 2.834589 mean absolute percentage error (MAPE) value and a 0.9706495 GINI index after execution on the prescribed data set. We compare the performance of the proposed model with other comparable models, and the simulation outcomes show that the model can generate the best results for MAPE, accuracy, and root-mean-square deviation (RMSE). [ABSTRACT FROM AUTHOR]

The G7 nations is a major global consumers of natural resources and energy. These nations are confronted with substantial obstacles in their pursuit of sustainable growth through energy transition, while upholding their status as global economic leaders. As a result, this current study conducted an aggregated and disintegrating analysis on the heterogeneous role of green energy innovation on energy transition in G7 nations. Green energy innovation was disintegrated into hydro, solar and wind energy. Moreover, the heterogeneous effects of green innovation in solar, hydro and wind on energy transition was investigated, an analysis that has not been previously examined. For the econometric estimation, we adopted the Quantile-on-Quantile Regression (QQR) and the quantile causality method. The results of the QQR indicates that the positive role of green energy innovation on energy transition in all of the G7 nations. However, this positive role is more dominant in five G7 nations, namely Italy, Germany, Japan, the UK and the USA. Additionally, the result also highlights that the connection between green energy innovation and energy transition is differs across the various quantiles for different nations. Moreover, the quantile causality analysis indicates that green energy innovation has the potential to predict future values in energy transition within the G7 nations. The study emphasizes various policy considerations for promoting energy transition through green energy innovation in G7 nation, which help realizing sustainable development. [ABSTRACT FROM AUTHOR]