Digital development is steadily creating new challenges and stress tests for the global energy infrastructure. However, with the emergence and popularization of mining activities and the proliferation of data centres, the condition and capacity of power grids have become critical issues for the proper functioning of lighting networks, as well as the entire national economy. The purpose of this study is to conduct a comprehensive study and assessment of the impact of mining activities and data centres on the condition of lighting networks, including an analysis of the sensitivity of LED equipment to power quality fluctuations caused by mining and server loads. The objective of the study is to identify key issues and pain points in the operation of lighting networks due to the proliferation of mining activities and data centres, as well as to identify possible actions to mitigate energy risks. The article concludes on the possibility of optimizing energy consumption when adapting mining data centres for data processing for the development of AI technologies. The key reasons for introducing regional restrictions on mining activities are also formulated, which are related specifically to issues of the stability of electrical networks, consumption meters, and ensuring the normal functioning of electronics and lighting equipment during peak loads.
Improving the energy efficiency of indoor lighting systems is a key strategy for reducing electricity consumption without compromising visual comfort. This study presents a performance assessment of Artificial Intelligence (AI) based daylight prediction models for open-loop indoor lighting control and compares their energy-saving impact with a reference closed-loop system. Real-world daylight data collected over a two-year period from a public building forms the basis of the analysis. The closed-loop system employs sensors at each measurement point, while the open-loop alternative leverages machine learning-based daylight prediction models using parameters such as room dimensions, wall reflectance, window size, measurement point distance from windows, and spatial orientation. Eight regression-based prediction models were developed and evaluated, among which the Support Vector Regression (SVR) model demonstrated the highest accuracy, while Theil-Sen Regressor yielded the lowest. The energy analysis was conducted specifically for electrical lighting electricity consumption, and results reveal that closed-loop control systems can reduce annual electricity consumption for lighting by up to 52 %, whereas open-loop systems achieve savings between 33 % and 40 %. Despite its superior performance, the closed-loop approach incurs higher installation and operational costs, resulting in a payback period of approximately from 8 to 10 years. These findings highlight the potential of AI-assisted open-loop lighting control systems as a cost-effective solution for retrofitting existing buildings.
T1his article provides a legal analysis of the use of financial leasing of lighting equipment in Russian public procurement and explores ways to integrate energy-performance incentives. The study aims to identify key shortcomings of the current lighting equipment leasing model for state and municipal needs and to substantiate the contractual structure of "energy-saving leasing" that links payments to actual energy savings. The methodological basis is an analysis of tender documentation for large-scale procurement under Federal Law No. 44, a comparison of the leasing and energy-performance models, and a doctrinal and regulatory interpretation of Article 327.1 of the Civil Code of the Russian Federation and the mechanism for standard contract terms. It is shown that Russian lighting equipment leasing practice reproduces the classic financial lease model with fixed payments, which ignores energy savings and shifts the risk of failure to achieve savings to the public client. The paper substantiates the possibility of transforming such contracts into a hybrid "energy-saving leasing" model by introducing a two-component leasing fee structure and tying the variable portion to the achieved savings. The practical significance lies in the proposal of a regulatory framework for codifying these terms in standard contracts, which simultaneously improves the energy efficiency of lighting modernization projects and reduces corruption risks in public procurement.
Simulating the solar radiation spectrum reflected by the Earth atmosphere for trace gas retrievals, such as CO2 and CH4, typically involves solving the radiative transfer equation (RTE), a computationally intensive process due to the multiple scattering term. To accelerate computations, we utilize a fast Python-based radiative transfer model FALCAS that approximates multiple scattering by introducing a virtual layer with isotropic scattering. This allows to mimic the results of the models with full multiple scattering treatment, yet to significantly reduce computation time. We develop an algorithm for retrieving CO2 and CH4 concentrations using FALCAS and evaluate its accuracy against a discrete ordinate model PYDOME. The CO2 and CH4 retrievals are performed using FALCAS from synthetic spectra generated by PYDOME in the (1500-1750) nm spectral range, considering various aerosol and geometric conditions. The error in retrieved concentrations is about 2.0 % for the aerosol optical thickness of about 0.9. This result remains robust under noise and convolution effects as well as across varying geometries, with errors under 5 % at a signal-tonoise ratio of 50.
As the population is increasing daily, the need for energy continues to increase gradually. Since tunnels require continuous lighting day and night, the energy consumed for the tunnel is quite high. Approximately 20 % of the energy consumed in T & uuml;rkiye is used in lighting. Road lighting has a high share of outdoor lighting. New-generation technologies in lighting are rapidly being adopted, followed, and implemented. Lighting systems are preferred to become smarter, more economical, and more comfortable. Many automations and smart technology solutions are used for energy efficiency and energy saving. One of the most effective solutions for the development of smart lighting systems is the use of universal protocols. In this study, information about the protocols for the control of LED luminaires, used in the lighting of tunnels, is given. In addition, the lighting of Hekimhan T1 Tunnel in Malatya Province is discussed, DALI (Digital Addressable Lighting Interface) and 0-10 V / 1-10 V Analogue Lighting Control Protocol are applied on this tunnel, and the results are analysed. The lighting design was made using the DALI protocol and then the amount of energy consumed for lighting, operating cost, maintenance and initial installation cost were calculated. The design also was made using the 0-10 / V1-10 V Analogue Lighting Control Protocol. Then, a comparison and recommendations were made.
