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Smart meters generate high-resolution data that can feed forecasting models for a range of distribution network applications. However, the literature remains fragmented across forecasting methods, data structures, and operational use cases. No comprehensive taxonomy links forecasting model characteristics to the distinct requirements of grid flexibility, microgrid management, and congestion control. This limits the ability of researchers and system operators to choose models according to data availability, forecast horizon, and grid function. This article develops a taxonomy of smart-meter forecasting models for distribution-level operation. The taxonomy maps model categories, data inputs, forecast horizons, and application domains into a structured classification framework. A systematic literature review of 27 peer-reviewed journal articles published from 2017 to 2024 was conducted. Models were classified by mathematical structure, data requirements, forecast horizon, and relevance to grid flexibility, microgrid energy management, and congestion control. The taxonomy organises forecasting approaches into classical time series, conventional machine learning, deep learning, hybrid and ensemble, and probabilistic forecasting categories. Six tables support the review method, model classification, smart-meter data mapping, application suitability analysis, full taxonomy, and research gap assessment. The taxonomy provides a structured decision-support tool for selecting and designing forecasting models for specific distribution grid tasks. It highlights gaps in cross-domain transferability, real-time deployment, benchmarking, data integration, and operational validation.

Microgrids need real-time energy management that integrates accurate load and solar forecasts with efficient battery dispatch while preserving battery lifetime. Forecast uncertainty can increase unnecessary grid imports, battery cycling, and curtailment when operational decisions rely on single-source prediction. This paper proposes a real-time forecasting and dispatch framework combining a hybrid load–solar prediction model with a degradation-aware battery scheduling strategy. The objective is to improve short-horizon operational decisions while reducing avoidable battery ageing. A hybrid forecasting model uses distinct methods for load and solar components. The dispatch strategy minimises operational cost while incorporating a degradation penalty derived from a cycle-life model. The framework is tested on representative microgrid data against persistence, statistical, and non-degradation dispatch benchmarks. The hybrid forecaster reduces prediction error compared to single-model approaches, and the degradation-aware dispatch extends estimated battery life by 8.6% while maintaining near-optimal economic performance. The framework also reduces unnecessary charge–discharge reversals during periods of volatile photovoltaic output. The integrated real-time system offers a practical, computationally light solution for microgrid operators, balancing cost, accuracy, and battery health. Its design supports deployment in campus, community, and commercial microgrids with limited computational infrastructure.

The rising priority placed on energy conservation, together with the adoption of renewable sources, has grown essential to sustainable practices across today’s university campuses. This research introduces an intelligent green energy management framework (SGEMS) that integrates machine learning predictions with reinforcement learning (RL) to enhance energy utilization and photovoltaic output on an eco-conscious campus. Leveraging historical records from three campus structures, the study developed a forecasting engine capable of projecting near-term electricity demand and solar yield. When paired with RL, the XGBoost method achieved markedly better accuracy in estimating consumption and production levels than competing algorithms, with root mean square errors (RMSEs) of 14.72, mean absolute errors (MAEs) of 12.00, and mean absolute percentage errors (MAPE) of 2.18%. The work also describes an accessible, browser-based dashboard that enables live energy oversight and supports timely decision-making, empowering facility managers to anticipate deficits and regulate consumption more intelligently. Overall, the presented methodology provides an adaptable blueprint that enables campuses to reduce dependence on utility grids, improve efficiency, and serve as a reference model for subsequent eco-friendly campus programs.

Microgrids provide an effective strategy for delivering energy while keeping expenses to a minimum. Within residential microgrids, individuals with diverse personality traits can collaborate in multiple ways. Additionally, the distinct decision styles arising from these personality differences can have a substantial impact on the success of their joint activities. The present study explores patterns of collaboration among neighbors with a range of personality traits within residential microgrids that form part of a smart-city ecosystem, thereby supporting energy exchange and reducing the total cost of energy provision. The methodology introduced in this work accounts for uncertainties arising from social conduct and organizes joint action through a game-theoretic structure. That structure works toward achieving an efficient and fair division of energy across participating neighbors, simultaneously driving down expenditures, encouraging cooperative behavior, and applying disincentives for non-cooperative conduct in line with each person’s behavioral profile. The suggested technique was tested on a residential benchmark microgrid using the GAMS CPLEX optimizer; two contrasting cases were examined to evaluate how energy storage affects trading decisions. By establishing mean values for discount and penalty rates, the method becomes better suited to diverse microgrid topologies. The analysis further addresses the tension between the utility company’s prices and the most advantageous rates from the neighbors’ perspective. Simulation results indicate that collaborative behavior enables neighbors to meet surplus requirements and can reduce the microgrid’s aggregate operating costs by up to 80%.

Effective energy management plays a vital role in improving greenhouse (GH) performance and advancing sustainability. This study introduces an innovative multi-objective optimization strategy specifically developed for GH energy management, aiming to reduce reliance on grid energy while maximizing the battery state of charge (SOC) over a defined time period. The optimization framework incorporates decision variables, including network power, battery power, and battery energy, while respecting constraints on battery capacity, initial energy levels, and the minimum and maximum energy limits of the battery storage system. The performance of a smart energy management system (EMS) is assessed by comparing it against conventional optimization techniques. Critical hyperparameters for the optimization process—such as plateau time, prediction time, and optimization time—are identified using the ellipse optimization technique. By modeling the GH as a microgrid, the study examines relevant energy management metrics and load profiles. Simulations performed in MATLAB Simulink (R2021b) reveal substantial improvements, with the smart EMS reducing the objective function by more than 50% relative to traditional EMS approaches. Furthermore, the proposed EMS demonstrates strong robustness under varying load power and irradiation patterns. Even in partial-shading scenarios, the system remains adaptable, with a maximum increase in the objective function of only 0.35553%. Matching the photovoltaic (PV) system output power to realistic field conditions further confirms the EMS’s practical effectiveness. These results highlight the smart EMS’s capability to optimize energy use in GH settings, paving the way for more sustainable energy management solutions. This work advances energy optimization techniques in agricultural contexts, supporting greater resource efficiency and environmental responsibility.

Platforms based on digital twins are gaining widespread use in overseeing energy infrastructure, particularly within smart grid environments. Recent progress in artificial intelligence opens up new possibilities to build greater user confidence by strengthening predictive and prescriptive analysis capabilities and refining how users interact with these systems. This study introduces an innovative framework and architectural design for energy digital twins that combines data-driven techniques with knowledge-based elements. Machine learning-driven data integration and analysis are fused into a knowledge graph that captures asset condition information, forecasting results, and relevant domain expertise. This structure underpins a retrieval-augmented generation strategy, which powers an advanced conversational virtual assistant grounded in a large language model. The assistant delivers practical decision-making support for asset oversight and maintenance activities. Key parts of the suggested architecture have been aligned with readily available commercial tools to create a working prototype. This prototype was applied in a practical scenario involving a portion of the high-voltage energy network located in central Italy.