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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.

Forecasting electricity usage is essential for boosting operational efficiency and advancing sustainability in buildings. The present work explores how the interval of data acquisition and the choice of modeling technique influence prediction precision for electricity demand in two contrasting building categories: an academic facility providing 15-minute data and an industrial facility providing hourly data. Multiple machine learning algorithms were tested—among them support vector machines (SVMs) with radial and sigmoid kernels, random forests (RFs), and deep neural networks (DNNs)—across various training/validation splits and feature combinations. Findings demonstrate that finer temporal resolution in data collection typically yields better model performance, reflected in lower RMSE, MAPE, and CV metrics, as well as higher R² values. Adding more lagged power consumption variables exerted a considerably stronger beneficial effect on forecast quality than incorporating weather-related variables. Notably, the SVM model equipped with the Radial kernel delivered the most consistent and superior results, proving especially effective at representing intricate nonlinear dependencies. By comparison, the DNN approach, despite acceptable performance on selected measures, exhibited notably higher MAPE values, raising concerns about potential overfitting. Collectively, these observations emphasize the need for deliberate decisions regarding data granularity, predictor variables, and algorithm selection to maximize forecasting reliability and thereby support smarter, more resource-efficient energy management in built environments.

Wind power (WP) is a Renewable Energy Source (RES) that has experienced remarkable growth as society continues to seek environmentally friendly energy alternatives. This study primarily explores Predictive Maintenance (PM) of Wind Turbines (WT). It evaluates the potential advantages of Wind Energy (WE) when integrated with the Internet of Things (IoT) and Wireless Sensor Networks (WSN). The research proposes an IoT-WSN framework for PM, structured into three key stages: the initial stage involves data acquisition via sensors; the second stage involves data transmission via an internet connection; and the third stage involves data analytics performed in a cloud computing environment. For PM analytics, this work presents a Predictive Maintenance Convolutional Long Short-Term Memory (PM-C-LSTM) model that integrates the spatial feature-extraction strengths of a Convolutional Neural Network with the time-series modeling capabilities of LSTM networks. The PM-C-LSTM model combines a CNN for spatial pattern detection with LSTM networks for sequential data processing, without compromising the precision of WT-PM. Additionally, a Failure Sample Generator model is incorporated to quantify soft failure and hard failure elements, thereby enhancing the predictive performance of the Machine Learning (ML) approach. The model was deployed on a Wind Farm (WF) on the Qinghai-Tibet Plateau, using data collected over 16 months. Comparative evaluations against several conventional models, based on multiple performance metrics, demonstrate that the PM-C-LSTM model delivers superior PM capabilities. The experimental results suggest that the combined use of IoT and ML has strong potential to advance PM strategies for WT, ultimately contributing to greater efficiency and sustainability in WE production.

Effectively integrating renewable energy sources into electricity markets is essential to aligning real-time supply with demand and reducing revenue losses from curtailment. Storage technologies offer an effective means to overcome this issue by boosting grid reliability and operational efficiency, while also enhancing market liquidity and dampening price fluctuations. In short-term electricity markets, participants face a wide range of choices, each with distinct risks and benefits, and well-designed trading strategies are crucial for profit maximization. This research investigates the optimization of trading activities in the day-ahead and balancing markets of the Irish electricity system over the period 2019–2022, using quantile-based forecasting techniques. By applying three trading strategies subject to realistic operational constraints, the study assesses different approaches, raises trading frequency, and incorporates flexible timestamp-based orders. The results highlight substantial profit opportunities from concurrent participation in both day-ahead and balancing markets, particularly with larger battery storage capacities. Even though higher trading volumes entail greater costs and compressed profit margins, the adoption of adaptive dual-market strategies proves instrumental in maximizing returns and overcoming market-specific obstacles. Lastly, a scenario-based analysis assesses the economic feasibility of four commercial battery storage systems, demonstrating that larger battery units deliver shorter payback periods. 

With the rise of the Smart Home sector and the growing adoption of Machine Learning (ML), the acquisition of power consumption data is becoming increasingly significant. Gathering real-world energy measurements using sensors is often time-intensive, costly, error-prone, and, in some cases, entirely infeasible. To address this, we introduce the VA-Creator, a framework designed for generating Virtual Appliances (VAs). These VAs produce synthetic power consumption patterns (PCPs) using Neural Networks (NNs) that dynamically adjust their architecture according to the structure of the training data, thereby facilitating the development of additional VAs. To cover every appliance category commonly found in a typical household, we employ a range of neural network types—including Multilayer Perceptrons (MLPs), Long Short-Term Memory networks (LSTMs), and a tailored Generative Adversarial Network (GAN)—alongside other ML approaches such as XGBoost, choosing the most suitable method based on the specific properties of each appliance. The outputs of these ML models are subsequently compared with actual measurements and evaluated using Dynamic Time Warping (DTW) and MLP-based discriminator classification accuracy as evaluation metrics. Furthermore, to verify that the VAs enable effective training of ML models, we generate synthetic datasets with them and conduct an in-depth evaluation by training Non-Intrusive Load Monitoring (NILM) models. The results of this comprehensive assessment confirm that the VA models yield realistic and practically useful outputs.

The swift expansion of electric vehicle adoption demands significant improvements in durability, safety, and dependability for advanced high-capacity battery systems. Consequently, there is a strong need for data-oriented battery management strategies capable of decoding intricate battery signals and guiding appropriate control of the coupled multi-physical processes inside batteries. At present, battery informatics is gaining traction as an effective strategy, harnessing sophisticated machine learning methods to generate reliable forecasts of battery capability, degradation state, and hazard levels—yet its advancement is severely constrained by insufficient data availability. In response, this work offers one of the pioneering contributions to synthetic and experimental data creation, integrating lab-based measurements with comprehensive three-dimensional (3-D) multi-physics simulations to establish a robust deep learning pipeline accompanied by detailed evaluation of performance metrics and thermal hazard anticipation. In particular, Part I addresses the construction and rigorous verification of the battery simulation framework, conducted in two main phases to confirm its predictive fidelity: initially, validation of the multi-physics approach was performed using measurements from two widely used commercial Li-ion cells—Panasonic NCR18650B and 18650BD; subsequently, an in-depth investigation of the thermal–electrochemical coupling was carried out, yielding valuable model-derived understanding such as temporal voltage profiles and identification of peak local temperature regions (hot spots). This carefully validated model produces high-quality, interpretable datasets suitable for training the hybrid convolutional neural network and long short-term memory architecture (CNN-LSTM), which will be detailed in Part II.

A wide range of strategies has been proposed to identify and prevent non-technical electricity losses resulting from fraudulent activities. Among these, machine learning algorithms and data-driven methods have become increasingly prominent compared to conventional approaches, owing to their superior performance, leading to growing adoption in recent years. This paper introduces a novel two-step method for detecting fraudulent Non-technical losses (NTLs) in smart grids. The initial step is to transform the time-series data by incorporating additional features extracted from the publicly available State Grid Corporation of China (SGCC) dataset. These features are derived following the detection of sudden changes in electricity consumption patterns, using the sum of finite differences, the Auto-Regressive Integrated Moving Average model, and the Holt-Winters model. Subsequently, five classification models are applied to train and evaluate a fraud detection system using the SGCC dataset. The performance evaluation shows that the Gradient Boosting Machine delivers the best results among the five models. This two-step procedure enables classification models to outperform previously reported top-performing techniques in terms of accuracy, F1-score, and other key metrics for non-technical loss detection.

Accurate short-term forecasting of household electricity consumption is vital to contemporary distributed energy networks. Most approaches to load prediction rely on deep neural architectures and require access to confidential, user-specific consumption records during training. Traditional neural network training typically collects all available data at a single central location to develop one unified model. Nevertheless, pooling individual user information in this manner raises serious concerns regarding data security and personal privacy. To address these issues, the present research explores federated learning techniques combined with model personalization as promising alternatives. Through a comprehensive simulation-based evaluation, these approaches are benchmarked against the standard centralized training strategy and a simple pre-trained reference model. The analysis reveals that the intrinsic characteristics and heterogeneity of the underlying electricity consumption patterns significantly affect the resulting model error levels. Consequently, directly contrasting error distributions across methods largely reflects differences in data composition rather than true differences in algorithmic effectiveness. To overcome this limitation, the study introduces a novel evaluation technique termed “differential comparison.” This approach focuses exclusively on pairwise loss differences across identical data instances, thereby enabling a fairer assessment of relative model improvements via the resulting difference distributions. Applying this method demonstrates that personalized model training outperforms all other strategies examined for residential load prediction, achieving better results in 59.1% of the evaluated scenarios.

Modern smart grids aim to deliver exceptional reliability and operational flexibility in power delivery. Contemporary electrical networks rely on synchronized wide-area monitoring tools, notably Phasor Measurement Units (PMUs), to supply instantaneous system status updates. These devices support prompt problem identification and corrective actions while ensuring uninterrupted grid operation. The surge in available measurement data has accelerated the adoption of machine learning techniques for tasks such as power system protection and state estimation. This work presents an intelligent fault detection and classification (FD&C) framework for transmission lines that employs an auto-encoder neural network architecture. The study evaluates and contrasts the capabilities of a Multi-Layer Extreme Learning Machine (ML-ELM) with those of a Stacked Auto-Encoder (SAE) neural network. These models are further benchmarked against established classifier approaches that rely on wavelet-based feature extraction, as well as against various other deep learning strategies. The proposed auto-encoder solutions attain perfect (100%) fault detection rates and achieve fault classification accuracies of 99.92% and 99.79%, all while demanding far less computation time during the testing phase. The ML-ELM variant stands out for its efficiency, maintaining excellent classification performance with both training and testing durations below 50 milliseconds. To reflect practical field conditions, the models were rigorously tested on datasets incorporating additive noise levels corresponding to signal-to-noise ratios (SNR) between 10 dB and 40 dB. Validation was performed using simulated data from the IEEE 39-bus benchmark system.

Advancements in technology, continuous innovation, and the emergence of the Industry 4.0 paradigm are driving Distribution System Operators toward increasingly competitive environments where flexible demand response mechanisms are essential. In many developing nations, however, the absence of standardized measurement frameworks across industrial process chains hinders the effective deployment of demand-side management models, leading to inefficiencies in data analysis and limiting the accurate assessment of large-scale consumers’ contributions to flexibility. To address this gap, the present study utilizes load profile data—specifically energy and power measurements obtained from utility meters—as input variables to generate disaggregated forecasts at 15-minute intervals across four defined time segments. The proposed approach is validated through performance metrics, with outcomes assessed using the Root Mean Square Error (RMSE) to quantify total methodological error. The framework integrates Machine Learning and Big Data techniques implemented in Python, employing Combinatorial Disaggregation Optimization alongside Factorial Hidden Markov Models to enhance prediction accuracy.