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Residential demand response is one of the key enabling technologies which plays an important role in managing the load demand of prosumers. However, the load scheduling problem becomes quite challenging due to the involvement of dynamic parameters and renewable energy resources. This work has proposed a bi-level load scheduling mechanism with dynamic electricity pricing integrated with renewable energy and storage system to overcome this problem. The first level involves the formulation of load scheduling and optimization problems as optimal stopping problems with the objective of energy consumption and delay cost minimization. This problem involved the real-time electricity pricing signal, customers load scheduling priority, machine learning (ML) based forecasted load demand, and renewable & storage unit profiles, which is solved using mathematical programming with branch-and-cut & branch-and-bound algorithms. Since the first-level optimization problem is formulated as a stopping problem, the optimal time slots are obtained using a one-step lookahead rule to schedule the load with the ability to handle the uncertainties. The second level is used to further model the load scheduling problem through the dynamic electricity pricing signal. The cost minimization objective function is then solved using the genetic algorithm (GA), where the input parameters are obtained from the first-level optimization solution. Furthermore, the impact of load prioritization in terms of time factor and electricity price is also modeled to allow the end-users to control their load. Analytical and simulation results are conducted using solar-home electricity data, Ausgrid, AUS to validate the proposed model. Results show that the proposed model can handle uncertainties involved in the load scheduling process along with a cost-effective solution in terms of cost and discomfort reduction. Furthermore, the bi-level process ensures cost minimization with end-user satisfaction regarding the dynamic electricity price signal.

Distributed energy resources have demonstrated their potential to mitigate the limitations of large, centralized generation systems. This is achieved through the geographical distribution of generation sources that capitalize on the potential of their respective environments to satisfy local demand. In a microgrid, the control problem is inherently distributed, rendering traditional control techniques inefficient due to the impracticality of central governance. Instead, coordination among its components is essential. The challenge involves enabling these components to operate under optimal conditions, such as charging batteries with surplus solar energy or deactivating controllable loads when market prices rise. Consequently, there is a pressing need for innovative distributed strategies like emergent control. Inspired by phenomena such as the environmentally responsive behavior of ants, emergent control involves decentralized coordination schemes. This paper introduces an emergent control strategy for microgrids, grounded in the response threshold model, to establish an autonomous distributed control approach. The results, utilizing our methodology, demonstrate seamless coordination among the diverse components of a microgrid. For instance, system resilience is evident in scenarios where, upon the failure of certain components, others commence operation. Moreover, in dynamic conditions, such as varying weather and economic factors, the microgrid adeptly adapts to meet demand fluctuations. Our emergent control scheme enhances response times, performance, and on/off delay times. In various test scenarios, Integrated Absolute Error (IAE) metrics of approximately 0.01% were achieved, indicating a negligible difference between supplied and demanded energy. Furthermore, our approach prioritizes the utilization of renewable sources, increasing their usage from 59.7% to 86.1%. This shift not only reduces reliance on the public grid but also leads to significant energy cost savings.

Predictive maintenance has lately proved to be a useful tool for optimizing costs, performance and systems availability. Furthermore, the greater and more complex the system, the higher the benefit but also the less applied: Architectural, computational and complexity limitations have historically ballasted the adoption of predictive maintenance on the biggest systems. This has been especially true in military systems where the security and criticality of the operations do not accept uncertainty. This paper describes the work conducted in addressing these challenges, aiming to evaluate its applicability in a real scenario: It presents a specific design and development for an actual big and diverse ecosystem of equipment, proposing an semi-unsupervised predictive maintenance system. In addition, it depicts the solution deployment, test and technological adoption of real-world military operative environments and validates the applicability.

A common problem that visitors have to face in big buildings, with several floors, corridors and departments, is their accurate location and orientation. The problem gets even worst when there are a big number of users and they have time constraints. A typical example is a medical centre where the patients have got scheduled doctor's appointments and, in some cases, severe movement difficulties. A possibility for solving this problem is to provide the building with an intelligent system for user detection and orientation. In addition, this strategy would allow to find and locate all the people inside the building and to carry out an individual search if it were necessary. This is the framework we have chosen in this article. We have developed a complete hardware and software system for people detection, location and orientation in this scenario. The hardware part of the system is based on the RFID technology and it has been successfully implemented in a fully operational prototype. The software uses artificial intelligence techniques, specifically planning and scheduling.

Wireless Sensor Networks (WSNs) are becoming increasingly popular since they can gather information from different locations without wires. This advantage is exploited in applications such as robotic systems, telecare, domotic or smart cities, among others. To gain independence from the electricity grid, WSNs devices are equipped with batteries, therefore their operational time is determined by the time that the batteries can power on the device. As a consequence, engineers must consider low energy consumption as a critical objective to design WSNs. Several approaches can be taken to make efficient use of energy in WSNs, for instance low-duty-cycling sensor networks (LDC-WSN). Based on the LDC-WSNs, we present LOKA, a LOw power Konsumption Algorithm to minimize WSNs energy consumption using different power modes in a sensor mote. The contribution of the work is a novel algorithm called LOKA that implements two duty-cycling mechanisms using the end-device of the ZigBee protocol (of the Application Support Sublayer) and an external microcontroller (Cortex M0+) in order to minimize the energy consumption of a delay tolerant networking. Experiments show that using LOKA, the energy required by the sensor device is reduced to half with respect to the same sensor device without using LOKA.