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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.

Precise prediction of load profiles—most notably the accurate identification of peak demand—remains difficult owing to the highly variable and unpredictable patterns of energy use. This work evaluates three forecasting approaches: Long Short-Term Memory (LSTM), Support Vector Regression (SVR), and a hybrid ensemble combining SVR, Gated Recurrent Units (GRU), and Linear Regression (LR) to generate 24-hour-ahead load forecasts. Individual load categories—household (HH), heat pump (HP), and electric vehicle (EV)—were treated together using full one-year consumption records. The research focuses on improving the reliability of load-profile predictions by comparing LSTM, SVR, and the proposed ensemble across HH, HP, and EV demand patterns. An innovative correction procedure is presented that refreshes the forecast every 8 hours, thereby improving forecast trustworthiness. Results underscore the value of deep learning techniques for better anticipating electricity demand, with particular strength in recognizing peak events, which in turn supports smoother and more cost-effective power system management. Both graphical assessments and quantitative validation metrics were analyzed, covering model behavior during off-peak hours, peak hours, and across the complete daily cycle. Across the tested approaches, LSTM delivered marginally stronger overall metrics in the majority of cases and demonstrated especially good performance at detecting peaks. That said, the hybrid model slightly outperformed LSTM when predicting EV-specific power demand, achieving an on-peak mean absolute percentage error (MAPE) of 21.45%, compared with 29.24% (SVR) and 22.02% (LSTM). Even so, graphical inspection clearly illustrated LSTM’s superior peak-capturing ability. This advantage was also evident in the on-peak mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) values, where LSTM improved by roughly 3–5% relative to both SVR and the hybrid model. In the final application, LSTM was used to forecast next-day load profiles based on real measurements collected from four separate grid areas and consistently demonstrated excellent peak-detection capability, with MAPE below 10% across most of those datasets.

This research proposes a novel, low-power routing protocol tailored for IoT networks that merges intelligent data-aggregation clustering with optimized path selection to overcome critical data-handling challenges in smart urban environments. The protocol relies on a layered architecture that includes Data Fusion Heads (DFH), intermediate relay DFHs, and the marine predators algorithm—an effective nature-inspired optimizer equipped with a purpose-built fitness function. This function simultaneously refines multiple criteria: the geographic clustering density of Sensor Nodes (SNs) inside each data fusion group (DFG), the total hop distance toward the sink, intra-group node proximity, residual battery level (RE), Average Scale of Building Occlusions (ASBO), and the rotation interval of the Primary DFH (PDFH). One of the protocol’s core advancements is the systematic application of in-network data fusion, which drastically reduces duplicate packet forwarding and markedly improves the accuracy and usefulness of collected information across all DFGs. By aggregating readings from multiple sensors, the volume of outbound traffic is greatly reduced, yielding substantial improvements in energy efficiency. Two complementary forwarding strategies are supported: single-hop forwarding, where already fused payloads travel directly to the sink, and multi-hop forwarding, in which an appropriate PDF relay is dynamically elected using a weighted relay selection metric that incorporates remaining energy (RE), sink distance, and ASBO. By integrating robust fault-tolerance procedures, intelligent redundancy elimination, and efficient fusion mechanisms, the protocol achieves markedly greater dependability and sustains strong operation under adverse or unpredictable network conditions. Detailed simulation experiments, along with side-by-side benchmarking against contemporary methods, confirm the protocol’s clear advantage across essential performance indicators—namely, network longevity, total energy expenditure, data throughput, and mean end-to-end latency. Compared with leading recent approaches such as the Particle Swarm Optimization-based energy-efficient clustering protocol (PSO-EEC), linearly decreasing inertia weight PSO (LDIWPSO), Optimized Fuzzy Clustering Algorithm (OFCA), and Novel PSO-based Protocol (NPSOP), the presented solution delivers outstanding gains. Concretely, network lifetime is prolonged by 299% versus PSO-EEC, 264% versus LDIWPSO, 306% versus OFCA, and 249% versus NPSOP. Energy usage drops by 254% relative to PSO-EEC, 264% relative to LDIWPSO, 247% relative to OFCA, and 253% relative to NPSOP. Throughput rises by 67% over PSO-EEC, 59% over LDIWPSO, 53% over OFCA, and 50% over NPSOP. By skillfully combining data aggregation with carefully tuned routing decisions, this protocol establishes a new benchmark for power-efficient organization and communication within IoT-based data fusion groups, delivering a dependable and adaptable framework suitable for diverse smart-city and IoT scenarios.

Over the past few years, integrating Distributed Energy Resources (DERs) with advanced communication infrastructures has created major challenges for the control and protection of electrical power systems. These difficulties stem mainly from the rapid evolution of smart grids and the growing threat of cyberattacks. As grid-tied solar Photovoltaic (PV) installations—equipped with smart inverters—become more widespread, their exposure to malicious cyber activities and the resulting risks to power network stability have become a pressing issue, especially given the inverter control logic. The present study examines the outcomes of targeted cyber intrusions targeting key PV system elements, with particular attention to the inverter and the Overcurrent Protection Relay (OCR). It further analyzes how these two components interact across a range of cyber-attack conditions. Simulations are conducted using a three-phase radial Electromagnetic Transients Program (EMTP) model to represent the grid and study transient responses during various cyber intrusions. Analysis results uncover the tight coupling between weaknesses in inverters and protection relays, demonstrating how disruption of one can severely affect the performance of the other. The work also quantifies how cyber incidents degrade the efficiency and operational reliability of PV systems connected to the grid. As a concrete illustration, when PV inverters were subjected to a brief 0.1 s pulse signal attack, the OCR failed to detect the abnormality and isolate the fault. This breakdown caused pronounced harmonic distortion together with considerable active power losses, stemming directly from the protection scheme’s inability to recognize and counteract the irregular disturbance. Overall, this investigation provides important new insights into cybersecurity challenges in grid-integrated solar PV installations. It underscores the urgent need to adopt stronger defensive strategies and build greater system resilience as power networks continue evolving into intelligent, interconnected smart grids.

This research investigates how perceptions of Thai government policies affect the willingness to adopt electric vehicles (EVs). Shifting toward EVs plays a key role in cutting greenhouse gas emissions and addressing climate change, in line with international sustainability targets. The study fills important gaps by examining the various dimensions of how people view government policies and their effects on EV adoption intentions within emerging economies, with a specific focus on Thailand. Data were collected through a survey of 3,770 participants nationwide from January to March 2024. The questionnaire evaluated several aspects of government policy perceptions, such as commitment and implementation efficiency, social welfare considerations, communication efforts, overall policy effectiveness, and tax-related advantages. The analysis applied techniques including Exploratory Factor Analysis (EFA), second-order confirmatory factor analysis (CFA), and structural equation modeling (SEM) to confirm the measurement constructs related to perceived government support and to test their impact on intentions to purchase or use EVs. Results indicate that concrete government actions—especially those that expand EV charging infrastructure and establish transparent regulatory frameworks—alongside clear, effective policy communication strongly shape public readiness to embrace EVs. The study also underscores the vital influence of perceived governmental dedication and financial incentives on consumer choices. Drawing on these conclusions, the research suggests that policymakers should focus on rapidly scaling up charging networks, making the government’s resolve more visible to the public, and strengthening direct monetary support to accelerate EV uptake. These insights enrich the literature on EV adoption dynamics in developing countries and provide actionable guidance for authorities aiming to advance eco-friendly transport systems.

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.