This paper outlines a modular system architecture that facilitates the intelligent integration of Electric Vehicle (EV) charging with Energy Management Systems (EMS) in workplace settings. The strategy focuses on establishing interoperable communication protocols, decision-making logic, and simulation tools to enhance coordination between energy demands and EV charging needs. The architecture comprises three main modules: EMS, Charging Station Management (CSM), and a Simulator. By utilizing synthetically generated data and MQTT-based communication, the system enables real-time energy distribution, simulations of various charging scenarios, and equitable power allocation through multifactor weighting strategies. A prototype developed in a lab setting showcases the architecture’s functionality and adaptability, illustrating its potential to address grid limitations, user preferences, and renewable energy availability in practical applications. This research opens the door for further exploration of automated negotiations among distributed systems for energy prioritization.

Integrating renewables into the grid introduces price fluctuations that stem from their inherent volatility. To mitigate this, adopting an energy-aware production planning strategy benefits factories and the grid. By aligning production processes with periods of abundant renewable energy and lower costs, factories can actively reduce their energy expenditures while simultaneously smoothing grid strain. However, such a transition requires expensive retrofitting of production planning systems. This paper proposes an approach to transparently integrating energy awareness into an existing Manufacturing Execution System (MES), resulting in a cost reduction without the need to retrofit the MES.

In condition monitoring, early detection of process signal drifts indicating, e.g., equipment degradation is crucial. exponentially weighted moving average (EWMA), cumulative sum (CUSUM), and discrete average block (DAB)-based drift detectors are statistical and commonly used methods. Each has benefits and limitations, suited to different data types. However, EWMA and CUSUM are fixed mean drift detectors, limiting their applicability and adaptability. This article explores adding dynamic behavior to drift detection methods. We use a wide range of synthetic data based on a real-world manufacturing process. The investigated parameter space includes standard deviation, drift rates, and outliers. Besides, each algorithm has some tuning parameters that define its behavior. Two metrics validate experiments against labeled data. Based on our observations, EWMA performs better for drift detection on average, but CUSUM is superior in detecting very small drifts. Furthermore, we derive guidelines for the choice and application of drift detection in practice.

The growing emphasis on environmental sustainability, driven by regulations such as Europe's Carbon Border Adjustment Mechanism (CBAM) and increased consumer awareness, is a notable trend. It is estimated that manufacturing energy consumption accounts for 24 % of global greenhouse gas emissions. As demand for renewable energy rises, current market mechanisms are unable to guarantee a carbon-free energy supply. This paper presents an approach that integrates energy availability and product carbon footprint (PCF) into production planning. The necessity for factories and energy suppliers to adapt their operations is highlighted. To ensure the optimal utilization of available energy resources, it is essential that factories implement production schedules that are responsive to fluctuations in supply. Energy suppliers must provide accurate long-term production profiles and employ anomaly detection for short-term adjustments. The proposed solution is designed to align energy consumption with renewable energy availability and PCF.

Modernizing street lighting is a logical reaction to dynamic economic changes and energy prices. The idea for intelligent street lighting is simple, complete control over the street lights depending on the conditions. At the same time, the main objective of this system is to lower energy consumption and, consequently, less CO2 emission. The number of cities which have established new lighting systems is growing fast because it is environment-friendly and economical. Modern lighting systems have different levels of intelligence and apply different strategies for changing energy consumption patterns. The impact of changing the lighting logic is much less than using intelligent street lighting or switching from conventional lights to LEDs. However, the total energy consumption for public lighting is too high that it is worth investigating any possible way for energy efficiency improvement, even for a fraction of one percent. This research explains a simulation-based approach in three real-world implementations for energy efficiency improvement using Street Lighting Simulation. In our studies, smart street lighting provides %25 to %60 energy savings, and optimizing the lighting strategy brings up to %5 more energy efficiency.

Energy consideration in production planning is becoming more important due to concerns over the climate change. However, the uncertainty of energy availability hinders the optimisation of production. The proposed concept uses an iterative approach of adjusting production scheduling in respect to the available energy using a multi-criteria optimisation that not only includes factory utilisation but also the likelihood of the predicted energy availability.

In smart manufacturing, the demand increases to be able to monitor and adjust process execution during production. It has led to a shift towards distributed, modular automation. A solution seems to be to use a cognitive architecture. It turns out that their generality often makes them unsuitable for specific industrial problems. In this paper, we propose a cognition-inspired architecture design for health monitoring tasks. The problem class is represented by a conveyor belt use case. We then discuss to what extent this architecture matches common implementations of cognitive theories by following a generalized cognitive process.

The main infrastructural parts of urban intersections are road marks, traffic lights, and lighting. Signaling - except for metal signs - is always realized as intelligent systems. In case of lighting systems, a trend for including intelligence into the luminaries for remote control and diagnosis applications can also be recognized. The increase in smartness comes at the cost of increased energy consumption. This paper describes a simulation first principle based system design of future intelligently managed street luminaries as smart lighting control system. Typical use cases were found and a simulation platform designed, to calculate estimated energy savings compared to regular LED-based light solutions, already reducing considerable lighting infrastructure costs. The paper presents a unique approach of mapping light area comparisons to simplified percentages and a promising simulation results of soon to be tested overall system energy efficiency increases by more than 20 % and CO2reductions without compromising traffic safety while maintaining and even increasing smart features of tomorrows critical infrastructure.