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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]

Featured Application: This review subscribes to the MiDiNA project as a part of the European Union-African Union partnership for renewable energy, 'LEAP-RE'. This review will shed light on the energy transition in North African countries and the resilient controls for improving power availability in microgrids. It seeks to draw attention to the advantages and drawbacks of each type of control as well as discuss the specifications for resilient controls that are necessary for microgrids in the North African environment. The ambition of making North Africa a hub for renewable energies and green hydrogen has prompted local governments and the private sector to work together towards boosting the growth of locally available, sustainable energy resources. Numerous climate and energy challenges can be addressed by microgrid technologies, which enable cost-effective incorporation of renewable energy resources and energy storage systems through smart management and control infrastructures. This paper discusses the ongoing energy transition in the countries of North Africa, highlighting the potential for renewable energy sources as well as regional obstacles and challenges. Additionally, it explores how robust and stable controls and advanced management strategies can improve microgrids' performances. Special attention is given to assessing the advantages and disadvantages of conventional and advanced controllers, with an emphasis on resilience needed within the harsh North African environment. [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]

There is no doubt that the transition towards renewable energies is generating many changes on different continents, some with greater impacts than others, but the development that has occurred is recognized and widely accepted. The progress has been significant but it is necessary to analyze the roadmaps that have been proposed so far at the island level so that decision makers have sufficient tools to commit the much-needed economic resources to transform their energy systems into 100% renewable ones. These approaches are not simple and the hard work of the authors who have disseminated their research is recognized. The roadmaps are planned based on the energy potential available in the territories and the future energy demand. Within countries, it is important to increase the economic resources to allocate to investments in environmentally friendly renewable energies. In this review of 100% renewable smart systems on islands, the situation of the American continent, its challenges and its long-term approaches in the different geographical areas facing 2050 are analyzed. This article shows that research into the design of 100% renewable energy systems in scientific articles is fairly new but has gained more and more attention in recent years. In total, 175 articles published since 2002 were identified and analyzed. Many of these articles have a predominant focus on the electricity sector. As a general result, it has been determined that although there has been significant progress towards an orderly energy transition, this has not been consistent with the international agreements signed since the Paris Summit, which is a real challenge in complying with the new commitment of the COP28 of Dubai in tripling the participation of renewables. [ABSTRACT FROM AUTHOR]

The proliferation of green industrial policy strategies around the world is taking place within a broader global context of a turn toward deglobalization and the pursuit of national self-sufficiency. The political economy of domestic renewable energy support and the basic principles of global trade regimes are in direct conflict, with direct implications for nations' abilities to transition to low-carbon economies. Many are calling for decoupling clean energy supply chains from China, yet such responses could slow the global clean energy transition, increasing the cost of deployment in developing countries. [ABSTRACT FROM AUTHOR]

The main objective is to propose a calculation method for assessing the benefits of individual domestic prosumers in self-consumption and economic savings when managing their own energy resources. The paper applies the demand-side management concept in the residential sector from the individual domestic perspective so that customers can understand the value of their own sustainable energy resources, conducting self-generation and demand management. The novelty lies in allowing the prosumer to manage their own energy resources to their benefit at a reasonable cost, instead of participating in automated large residential demand-side-management programmes that respond to the means of the grid system operator or other energy service companies, such as aggregators. A methodology for calculating the self-consumption rate and the economic benefit for the consumer is proposed, including three different cases: consumer demand is higher than self-generation, and consumer demand is equal to self-generation, and consumer demand is lower than self-generation. The methodology is validated with actual data from a household in Valencia (Spain) during a complete year, obtaining an average reduction in the annual electricity bill of 70% and a demand coverage with the self-renewable system reaching values of 80% throughout the year. The significance of this methodology goes beyond the economic revenue of the individual consumer; it also aims to guide consumers towards efficient practices in the use of their available energy resources and raise awareness on their energy behaviour. [ABSTRACT FROM AUTHOR]