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The study aims to present the effectiveness of anomaly detection algorithms in lighting systems based on analyzing records from electricity meters. The road lighting management system operates continuously and in real-time, requiring online anomaly detection algorithms. The paper examines two machine learning-based algorithms: Autoencoder with LSTM-type recurrent neural network and Transformer. The results obtained for these algorithms are compared with a simple mechanism for comparing energy consumption in consecutive periods. Classification metrics such as error matrix, sensitivity, precision, and F1-score were used to evaluate the performance of the algorithms. The analysis showed that the Autoencoder algorithm achieves better accuracy (F1-score = 0.9565) and requires significantly fewer computing resources than the Transformer algorithm. Although less efficient (F1-score = 0.8125), the Transformer algorithm also demonstrates the ability to detect anomalies in the road lighting system effectively. Implementing the Autoencoder algorithm on an actual ILED platform allows anomaly detection with a delay of 15 min, which is sufficient to take corrective action. The conclusions of this study indicate the significant advantage of machine learning-based algorithms in anomaly detection in lighting systems, which can significantly improve the reliability and efficiency of urban lighting management.

This paper proposes energy-efficient solutions for the smart light-emitting diode (LED) lighting system, which provides minimal energy consumption while simultaneously satisfying illuminance requirements of the users in a typical office space. In addition to artificial light from dimmable LED lamps, natural daylight coming from external sources, such as windows, is considered as a source of illumination in an indoor environment. In order to reduce total energy consumption, the smart LED system has the possibility to dim LED lamps, resulting in reduced LED output power. Additionally, various LED lamps’ functionality, such as semi-angle of the half illuminance and LED tilting, are introduced as an additional parameter to be optimized to achieve greater energy saving of the designed system. In order to properly exploit external lighting, the idea to reduce overall daylight intensity at a users’ location is realized by the option to dim the windows with a shading factor. Based on the users’ requirements for a minimal and desired level of illumination, the proposed optimization problems can be solved by implementing different optimization algorithms. The obtained solutions are able to give instructions to a smart LED system to manage and control system parameters (LEDs dimming levels, semi-angles of the half illuminance, orientation of LEDs, the shading factor) in order to design total illumination, which ensures minimal energy consumption and users’ satisfaction related to illuminance requirements.

This study presents a review of smart light-emitting diode (LED) lighting systems applied to smart buildings. The study is focused on drivers, protocols, technologies, communication networks and applications. An extended overview of the methodologies used for LED lighting control in smart buildings is addressed. The study also presents an integrated architecture able to achieve the necessary services and control methodologies for intelligent building energy management system for LED lightings systems in smart buildings.

Daylighting systems make daylight illuminance possible, and the development of prototype daylighting systems can provide more efficient daylight illuminance. The purpose of this article is to review the development and performance of prototype daylighting systems in the last decade. The passive and active daylighting systems are listed separately and divided into the four categories by the presence and absence of hybrid. Each prototype daylighting system was evaluated in terms of cost and daylight performance and as well as their novel optical design. We evaluated the architecture and daylighting principles of each system by reviewing individual prototype daylighting systems. The cost of prototype systems still poses a challenge to development. How to use passive or active systems in different environments and whether or not electrical lighting assistance is needed is a controversial issue. However, active daylighting systems equipped with solar tracking systems are still mainstream. This research is a valuable resource for daylight researchers and newcomers. It is helpful to understand the advantages of various prototype daylighting systems and commercial daylighting systems that have been developed for many years; moreover, it is also possible to know the research directions suggested by the prototype daylighting systems. These will be of further use in developing innovative and better daylighting systems and designs.

In this paper, a digital twin-based assessment framework is proposed to determine which energy-saving technologies and strategies will work best in existing buildings. The proposed framework is based on a digital twin that integrates the existing building’s hardware system, the building’s operational schedule database, and a probabilistic model of occupant behavior. A digital model was constructed based on field measurements and database integration for a case study involving nine university buildings and 55 classrooms. As a result, in the classrooms involved in the case study, the lighting was turned on in the absence of occupants for an average of 10.7 h a day. The results indicate that it is very important to turn off the lights after the last hour of use in university classrooms in South Korea and that it is possible to reduce power consumption by more than 60% by employing an off strategy involving a passive infrared sensor or manager. Additionally, LED lighting in most classrooms is over-designed, which indicates that 46% of the energy consumed can be saved by adjusting the luminance level to an appropriate range.

To enhance efficient and sustainable energy usage in street lighting systems, a nano-grid infrastructure comprising an energy harvesting, storage, and management system is integrated. This paper investigated the feasibility in terms of energy production and economic evaluation of using various energy harvesting for photovoltaic, piezoelectric, and wind energy in a nano-grid street lighting system. The photovoltaic system was evaluated based on the factors of annual actual solar radiation, power losses, and system performance using the PVsyst software. The piezoelectric energy production was studied and designed. Optimal piezoelectric installation for maximum power generation was analyzed in terms of deformation and stress using ANSYS software. For wind power generation, the wind turbine characteristics, along with its location, were designed to optimize power output using computational fluid dynamic simulations in ANSYS software. After that, economic evaluation for the proposed energy harvesting systems for nano-grid street lighting system are analyzed and compared in terms of DPP, NPV, IRR, and LCOE. In addition, the optimization of using PV, a wind system, a hybrid PV—wind system for nano-grid street lighting systems was conducted using HOMER Pro software. The results indicated that generating power through PV, piezoelectric, and wind energy was feasible. However, economic evaluation unveiled the infeasibility of employing piezoelectric and wind energy systems due to their elevated investment costs relative to their power generation capabilities. The dynamics of power generation from PV and wind systems, along with street lighting consumption, significantly impacted the dimensions of energy harvesting and storage systems, as well as their economic feasibility. The hybrid PV-wind system exhibited strong economic feasibility.

The field of indoor lighting covers a wide range of lighting uses with varying requirements for lighting conditions to be satisfied by properly selected lighting equipment. The need to frequently change the arrangement of useable areas entails the necessity to adapt the lighting to new requirements. A good solution for reducing costs and saving time is a luminaire adjusting the luminous flux and spatial luminous intensity distribution in a wide range. The authors present the concept of an adaptive luminaire and its construction assumptions. In addition, the results of studies on the development of the concept are shown together with conditions and limitations that influenced the construction of the luminaire. The analysis of the surface of the moveable reflector is presented, and the results of testing the luminaire prototype are compared with the results of simulation tests.

When designing headlights using optical software LightTools, we found that the maximum illuminance and facula diameter of the initial headlamp lighting system design were less than 20000 lux (maximum illuminance) and 40mm (facula diameter), which did not meet the minimum performance requirements. However, if we increase the maximum illuminance value, the facula diameter value will become smaller and may not meet the minimum performance requirements. In order to obtain the best value of maximum illuminance and facula diameter at the same time, we used a multi-objective optimization method (combination of Taguchi method and robust multiple criterion optimization approach (RMCO)) to solve maximum illuminance and facula diameter optimization problem. It is effective and helpful increase the maximum illuminance and facula diameter of the headlamp lighting system (The maximum illuminance increased from 9580 lux to 22900 lux, the facula diameter increased from 40mm to 50mm). In this method, the concept of statistical analysis is used to obtain a set of diversified Pareto-optimal solutions. The set of diversified Pareto-optimal solutions can obtain for energy optimized design of headlamp lighting system. This study is the first attempt to use the RMCO method to optimize the multi-objective problem in a headlamp lighting system.

Smart meters in road lighting systems create new opportunities for automatic diagnostics of undesirable phenomena such as lamp failures, schedule deviations, or energy theft from the power grid. Such a solution fits into the smart cities concept, where an adaptive lighting system creates new challenges with respect to the monitoring function. This article presents research results indicating the practical feasibility of real-time detection of anomalies in a road lighting system based on analysis of data from smart energy meters. Short-term time series forecasting was used first. In addition, two machine learning methods were used: one based on an autoregressive integrating moving average periodic model (SARIMA) and the other based on a recurrent network (RNN) using long short-term memory (LSTM). The algorithms were tested on real data from an extensive lighting system installation. Both approaches enable the creation of self-learning, real-time anomaly detection algorithms. Therefore, it is possible to implement them on edge computing layer devices. A comparison of the algorithms indicated the advantage of the method based on the SARIMA model.

Artificial lighting systems are an integral part of production halls. These systems are typically used to supplement daylight, although in some cases they are the sole source of lighting in the workplace. Every artificial lighting system is accompanied by an unfavourable phenomenon: glare. This phenomenon is one of the main factors affecting the quantitative parameters of internal lighting systems. Glare needs to be reduced in each production area to ensure visual well-being, which is linked to the visual performance of employees and consequently affects the efficiency and productivity of production. Obtaining information about glare in a real system is a difficult and lengthy process. However, digital software tools can be used in a simulated environment to predict and reduce glare. This article addresses the issue of reducing glare in artificial lighting systems using a digital software solution. The introduction discusses the theoretical aspects of glare caused by artificial lighting systems. This is followed by a practical example using the simulation method with the Dialux Evo tool to reduce glare in the workplace. The conclusion of the article provides an analysis of the results achieved and offers recommendations for practical implementation.