This master thesis work is aimed to investigate the possibilities of achieving more efficient industrial lighting. The study is divided in four parts: 1) Industrial lighting energy efficiency measures, 2) Added value of lighting, 3) Drivers and barriers for adopting lighting efficiency measures, and 4) Applications of AI in industrial lighting.The first part of the study explores various energy efficiency measures that could be applied in industrial lighting. The results show that using energy-efficient lighting fixtures, optimizing lighting controls, and adopting smart lighting solutions that integrate daylight in the illumination strategy and design are the most effective measures for reducing energy consumption and increasing efficiency.In the second part, the study examines the added values or non-energy benefitsof efficient industrial lighting. The findings indicate that apart from cost savings, efficient lighting leads to improvements on the quality of work environments, enhances workers health and safety conditions and improves environmental performance. Moreover, the study suggests that in many cases, the added values of lighting are not given the importance they should have and are not considered when an energy efficiency investment is planned to be done.The third part of the study identifies the drivers and barriers for adopting lighting efficiency measures in industrial settings. The study found that factors such as cost and energy savings, energy efficiency regulations are the main drivers for implementing efficient lighting solutions. However, barriers such as lack of awareness, perceived high initial costs, technology adoption and insufficient government incentives are the main obstacles to adoption.Finally, the study investigates the potential of artificial intelligence (AI) in industrial lighting. The results show that AI-based solutions, such as predictive maintenance and intelligent lighting control could significantly improve energy efficiency and reduce maintenance costs. Moreover, AI can bring the work environment to another level by the application of human centred and personalized lighting.Overall, this master thesis work provides valuable insights into achieving more efficient industrial lighting by highlighting effective energy efficiency measures, identifying the added value of efficient lighting, and examining the drivers and barriers to adoption. Moreover, the study sheds light on the potential of AI in industrial lighting and its potential benefits and future challenges.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-195691 |
| Date | January 2023 |
| Creators | Aiastui, Xabier |
| Publisher | Linköpings universitet, Energisystem |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0021 seconds