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Day-ahead distribution-grid operation requires anticipating customer demand, renewable generation, network loading, and available demand-side flexibility. Smart-meter data provide a practical basis for identifying recurring consumption patterns and embedding those patterns into operational scheduling. This article presents an original forecasting-optimization decision-support system for day-ahead distribution-grid operation. The system integrates smart-meter clustering, renewable prediction, and flexible load scheduling to support distribution system operators. The proposed system processes smart-meter profiles into consumer clusters, predicts day-ahead photovoltaic and wind generation from weather-driven features, and schedules flexible loads through constrained optimization. The decision-support workflow is designed to produce feeder-level schedules, expected voltage conditions, congestion indicators, and operator-facing recommendations. The clustering module identifies consumer groups with different peak timing, ramping behavior, and flexibility availability. The integrated DSS is structured to reduce peak loading, improve voltage profiles, and lower operational cost compared with a baseline day-ahead plan without forecast-informed scheduling. The proposed DSS offers a practical framework for data-driven distribution-grid operation. It supports renewable integration by translating forecasts and customer flexibility into actionable schedules for day-ahead planning.

Reliable forecasting of electric vehicle (EV) charging time is vital for improving driver convenience and enabling smart, real-time charge scheduling. The present work introduces a comprehensive dynamic prediction system for EV charging duration that integrates four complementary modules: data preparation, pattern identification, baseline forecasting, and adaptive error adjustment. Raw measurements from the Battery Management System (BMS) are first cleaned and scaled to correct for missing or erroneous entries. A specially designed convolutional autoencoder tailored for EVs (EV-CAE) then extracts features across multiple time scales, after which K-Means clustering groups the sessions into characteristic charging profiles. Drawing on these profile assignments, the baseline forecasting stage computes an initial estimate of remaining charging time using past session records and the assigned cluster label. For improved resilience to changing circumstances, an adaptive correction layer is incorporated; it blends linear regression, logarithmic fitting, ratio-based scaling, and neural-network-driven refinement to update the forecast during the session continuously. Validation on authentic EV field datasets reveals that the overall architecture delivers a clear gain in forecast quality. In particular, activating the adaptive correction layer increases the coefficient of determination (R²) from 0.948 to 0.960. It maintains consistent accuracy even when charging curves vary unexpectedly or when operating in cold-weather conditions. These outcomes highlight the framework’s readiness for deployment and its engineering relevance in next-generation intelligent EV charging infrastructure.

In large cities with evolving infrastructure, optimizing water supply systems is critical. However, tackling these issues becomes considerably more difficult without a thorough understanding of the network layout and water-use patterns. This paper introduces a methodology based on geospatial data analysis to address two primary objectives. The first is the definition of service zones for infrastructure facilities to improve overall system manageability. The second is the development of an approach to determine the optimal locations for devices that collect and transmit hydraulic network parameters, ensuring their placement aligns with both water supply sources and the areas they serve. The research utilizes data from the water supply network of a city with a population of more than half a million, applying hierarchical clustering with Ward’s method to examine spatial distribution. Four territorial clusters were distinguished, each exhibiting distinct characteristics related to consumer density and water consumption volumes. The resulting cluster boundaries were compared with the system’s current service scheme, demonstrating consistency with the actual infrastructure. Clustering quality was additionally assessed using the silhouette coefficient, which confirmed the high precision and dependability of the selected method. The study illustrates the value of visualizing cluster boundaries in evaluating the even placement of pressure sensors throughout the urban water supply network. The findings indicate that combining geographic data with water consumption details not only supports efficient infrastructure planning and resource distribution but also lays the foundation for the digital transformation of the hydraulic network—an essential element of sustainable progress in contemporary smart cities.

The Smart Grid (SG) seeks to overcome the limitations of conventional power systems by integrating renewable energy sources. It leverages wireless sensor networks (WSNs) to improve monitoring of power generation equipment. However, the open and expansive deployment of WSNs in SG introduces new vulnerabilities and potential attacks. This paper introduces a novel security framework for WSNs in SG that utilizes public key infrastructure (PKI). The core concept involves distributing the duties of the certification authority (CA) across multiple sensor nodes to guarantee service availability and scalability. A new lightweight clustering method is developed to select these nodes, accounting for their trust values and remaining energy. This framework delivers essential security features, including authentication and data confidentiality, while countering threats such as Sybil attacks and eavesdropping. Comprehensive simulations with OMNET++ and the Castalia framework demonstrate the clustering algorithm’s effectiveness: nearly all sensors join clusters, and the number of cluster heads remains consistent even when malicious nodes are present, highlighting the framework’s resilience.