This study presents a year-long, multi-sensor residential monitoring dataset from a composite-climate home in Mohali, India, integrating appliance-level electricity use, indoor temperature–humidity measurements, and outdoor weather observations into an hourly residential energy informatics workflow. Nine appliances were monitored at 5-minute resolution and processed through a reproducible pipeline involving timestamp harmonization, anomaly handling, multi-source alignment, and feature extraction, yielding an analytics-ready dataset spanning 8,736 hourly observations (July 2024–June 2025). The monitored appliances consumed 845.25 kWh (58% of total household electricity use), with the refrigerator (21.95%) and air conditioner (cooling + heating; 19.07%) as the dominant monitored end uses. Psychrometric mapping quantified mixed-mode operation, distinguishing operating regions for Air Conditioner (AC) cooling (n=460 hours), evaporative cooling (n=172 hours), and AC heating (n=45 hours). Weather sensitivity analysis showed strong temperature dependence in baseline and seasonal loads (effect of outside temperature on refrigerator r=0.793; effect of outside temperature on geyser r=-0.694; slope -0.048 kWh/$$^{\circ }$$C), demonstrating the value of fusing weather variables into appliance analytics. Indoor comfort evaluation using the India Model for Adaptive Comfort (IMAC) indicated that 80.8% of summer hours and 51.7% of monsoon hours with valid measurements fell within adaptive comfort limits, with winter interpretation constrained by modeling assumptions and sensor availability. The primary contribution is a transparent, transferable workflow that converts heterogeneous residential sensing streams into a unified analytics dataset suitable for season-responsive appliance modeling, comfort-aware interpretation, and future multi-home deployments.

The increasing computational requirements in modern Artificial Intelligence (AI) development have raised the stakes when it comes to the environmental sustainability of machine learning use and application. The topic that has received relatively little treatment in previous research is the carbon footprint of AI systems, but it is not studied continuously across algorithms, hardware, life cycle, and use. This work takes stock of the state-of-the-art to provide a comprehensive review of carbon-aware AI from the bottom up for the entire gamut of computation. Consistent with PRISMA principles, by way of systematic search across prominent academic databases and repositories (2018–2025), we identified 784 unique citations and extracted 62 studies that satisfied pre-established inclusion criteria. These studies were organized by four domains: algorithms for energy (20), hardware and accelerators (15), Life-cycle assessment (LCA) (9), and operation under deployment (11). Three major conclusions follow from the synthesis. First, algorithmic efficiency – including pruning, quantization, and sparsity might reduce computational burden to meet carbon goals; however, they only achieve concrete carbon reduction when accounted for in hardware and data centre setups. Second, life-cycle analyses show that while operational energy continues to account for most emissions during large-scale training, the embodied carbon from semiconductor fabrication plays an increasingly important role in fleets with a lot of equipment or frequent refreshes. Third, deployment decisions such as data center location, carbon-aware scheduling, and cloud–edge workloads placement bring much more variance on real emissions compared to what can be achieved at model-level optimisation. Between sectors, inconsistencies in methodology – notably for carbon reporting, system boundaries, and energy telemetry – hinder reproducibility and comparison of findings across studies. To overcome these limitations, this review suggests a research agenda with respect to standardized carbon accounting, hardware–software co-optimization, better available embodied-emission data, and the inclusion of carbon in decision-making for AutoML and scheduling systems. This comprehensive integration will serve as a stepping-stone towards the development of sustainable AI across academia and industry.

This paper proposes a decentralized operational planning framework for integrated transmission and distribution (TN–DN) networks that enables coordinated yet autonomous decision-making between network operators. A fully nonlinear alternating-current optimal power flow (AC-OPF) model is formulated for both TN and DN subsystems to capture voltage and reactive-power constraints accurately. A iterative coordination mechanism is introduced to preserve data privacy while ensuring operational consistency through limited exchange of active and reactive power at boundary buses. To solve the resulting high-dimensional, highly constrained nonlinear optimization problem efficiently, an enhanced particle swarm optimization (PSO) algorithm is developed, incorporating time-varying learning coefficients, adaptive local search, and chaotic diversity control to accelerate convergence and improve solution quality. In addition, a robust max–min optimization strategy is integrated to identify worst-case sudden generator outages without relying on scenario enumeration or probabilistic assumptions. The approach is validated on standard IEEE test systems. Numerical results show that the decentralized framework attains solutions close to centralized optimality while lowering computational effort, improving scalability, and enhancing resilience to generator contingencies, demonstrating its practical suitability for coordinated TN–DN operation planning.

This study presents a critical and structured review of protection challenges and emerging solutions in renewable-integrated microgrids. The proliferation of distributed energy resources (DERs) has introduced complex protection issues, including bidirectional power flow, low fault currents, mode-dependent dynamics, and communication dependencies. This review systematically examines fault detection, classification, and coordination strategies for both grid-connected and islanded operating conditions from 2020 to 2025. A qualitative comparative analysis is conducted across diverse protection approaches, including conventional relay-based methods, artificial intelligence (AI)-based schemes, fuzzy logic systems, time-frequency analysis, phasor measurement unit (PMU)-assisted techniques, and communication-assisted multi-agent frameworks. The analysis identifies dominant trends toward hybrid, adaptive, and data-driven protection strategies while highlighting persistent gaps in scalability, experimental validation, cybersecurity, and real-world deployment. By synthesizing current research and identifying unresolved challenges, this review provides a clear roadmap for future work toward robust, standardized, and practically deployable microgrid protection systems.

Abstract Electricity markets increasingly rely on residential demand-side flexibility to integrate renewables and stabilize the grid. While dynamic tariffs can unlock short-term flexibility, they expose households to a risk–reward trade-off. This paper quantifies how home battery storage reshapes the trade-off across residential energy services modeled with three different load types (elastic, interruptible and non-interruptible). Using load profiles from a German utility and an optimal-control scheduling framework under mixed dynamic tariffs, we evaluate cost and risk impacts over a range of storage sizes. Three results stand out. First, small batteries deliver most of the value: a capacity of about 20% of average daily demand captures roughly two-thirds of attainable savings while already lowering bill risk. Second, cost reduction potential is heterogeneous across devices: Elastic loads profit the most from additional storage capacities ; Non-interruptible and Interruptible loads profit less. Third, overall returns diminish and effectively plateau near a capacity of 60% of average daily demand. These findings offer actionable guidance: pair dynamic tariffs with modest storage to achieve substantial savings and risk reduction—especially in low-flexibility or strongly market-aligned households—and avoid over-investment in regards to diminishing returns.

This paper presents a novel hybrid machine learning method for enhanced islanding detection in distributed generation systems. The proposed approach integrates distinct feature extraction from voltage and current signals at the point of common coupling with an optimized Extreme Gradient Boosting (XGBoost) classifier to accurately differentiate islanding events from normal grid disturbances. Validated on a public residential microgrid dataset, the method demonstrates superior performance by achieving a high detection accuracy of 97.83%, effectively eliminating the non-detection zone, and maintaining a detection time of under two cycles. This approach provides a robust, non-intrusive, and computationally efficient solution for anti-islanding protection, significantly outperforming conventional passive techniques, while the use of a publicly available dataset ensures full reproducibility and offers a benchmark for future research.

This paper proposes a Hybrid Stackelberg-Markov framework for adaptive load scheduling and dynamic pricing in smart grids. The framework integrates a Stackelberg game to model the interaction between the utility and consumers with a Markov process that captures consumer behavioral dynamics. By combining economic incentives with behavioral adaptation, the model achieves a balance between reducing the peak-to-average ratio (PAR), lowering consumer costs, and increasing utility profit. Simulation results demonstrate that the proposed approach reduces PAR by 43% compared with the baseline, decreases average consumer costs by 28%, and improves utility profit by 10%. The behavioral state analysis further shows that most consumers transition into the Content state, indicating long-term acceptance of dynamic pricing strategies. Moreover, the computational analysis confirms faster convergence and reduced run time compared with conventional demand response schemes. These results establish the proposed framework as a scalable and practical demand response solution for modern smart grids.