Imagine, when you walk into a house, the lights suddenly come on, all kinds of furnishings in the house interact with you as if they are conscious, and all kinds of electrical machinery start magically… If you met in your childhood When you come to such a house, I am afraid that most people will think that it is haunted. But this is now, and the place isn’t haunted at all, so there’s no need to bother Ghostbusters 1.
In fact, telecontrolling objects is a perfectly logical thing to do. As engineers, we are well aware of the development of artificial intelligence (AI) in recent years. Artificial intelligence is a technology that enables computers and machines to imitate human behavior. An important part of it is machine learning (ML), which is to combine artificial intelligence technology with statistical processing technology, and gradually move towards the goal in computers and machines. In the process of continuously improving them, so as to achieve the purpose of letting them “learn”.
AI affects nearly every field and application, and the cities and spaces we occupy are no exception. In a smart city, smart sensing and processing networks, combined with artificial intelligence and machine learning technologies, will work to improve the environment in which we live, as well as in our home, work and play environments. Smart cities will impact lighting infrastructure, buildings, utilities, transportation, the environment and communications across the city, promising to change the way we interact with the world around us.
Some forecasts state that lighting accounts for one-sixth to one-fifth (1/6-1/5) of total energy consumption today. The arrival of AI enables building lighting to be controlled and automated to save costs, reduce energy consumption and waste, and improve service quality and customer satisfaction. Artificial intelligence is like an invisible intelligence worker, assisting in various decisions and making the smart buildings of the future a reality. So how exactly does the invisible AI control the visible lighting environment? Let’s break it down in detail (Figure 1).
Figure 1: AI will impact the future of smart lighting control and automation
Introduction to Smart Lighting
Communication and control systems are integrated into smart lighting systems, and they allow for greater automation and flexibility. In addition, wireless communication can be added to allow the entire smart lighting system to cover longer distances. They all help to improve the following three areas:
Overall (macro level)
edge (local level)
specific (device level)
When these issues are improved, it helps to improve the overall responsiveness and efficiency of the lighting system.
Smartphones, computer systems or wall-mounted units can all be used as control and switching stations for lighting systems. By adjusting the combination of red, green, blue, and white light, the color or white level of the light can be adjusted to provide the specific wavelength and correlated color temperature (CCT) required. By adjusting the output light level, it is possible to control the optical power emitted from various locations. General luminaires, recessed luminaires, architectural (indoor or outdoor) lighting, signage and landscape lighting can all be coordinated in one system.
Machine learning makes lighting better
If I want to learn all kinds of knowledge, I have to study for several years. Some things, like the basic grammar of speaking, I mastered at a very young age, but more in-depth stuff, like quantum physics and dealing with Laplace transforms like arithmetic addition, took me years to learn. accomplish.
Artificial intelligence is a major disruptive technological innovation that has pioneered the ability to bring learning to smart lighting systems, enabling them to improve their performance like feedback in Electronic circuits. This learning and refinement capability is machine learning.
Machine learning often requires the use of large amounts of data. When analyzing data, computers need to make their own decisions. These decisions are called “inferences,” which are conclusions based on evidence and logical reasoning. This type of processing is well suited to be performed by a computer.
Computer systems can learn in three ways:
Supervised learning works by providing the desired best correct answer (output) and comparing it; in contrast, unsupervised learning, which complements it, does not provide any information about the desired best correct answer (output). Reinforcement learning provides appropriate positive or negative feedback based on what should be the best correct answer (output). Because of the computational power of computers, computers can often achieve extremely rapid improvements in reinforcement learning compared to humans without computer assistance (Figure 2).
Figure 2: Future smart lighting systems will use artificial intelligence to mimic human behavior and learn how to operate autonomously
The adoption of artificial intelligence in lighting systems
Today, various industries are actively adopting AI, with banking, retail, automotive and healthcare being the first to adopt AI in their respective fields. Clearly, while AI will be ubiquitous, it will not be adopted at the same pace across industries. Over time, the knowledge and lessons learned in these areas will benefit industrial applications.
The field of industrial control, including smart lighting, covers an enormous breadth and scope. If an organization has a unique understanding of smart lighting controls and automation parameters, it will adopt AI faster than those that delegate this responsibility to an outside company. Implementing AI and machine learning is easier for organizations that understand how learning algorithms can solve challenges and achieve goals from the start. If there is an understanding of the limitations and interrelationships of existing systems, it will undoubtedly help to identify specific areas in which artificial intelligence needs to be focused and applied in building lighting control and automation solutions. By tailoring AI, organizations can solve problems they want to control and automate in specific application areas. AI is a versatile tool that, like a handyman with a plethora of sophisticated tools, can handle a wide variety of contexts and applications.
Activities in the industrial sector are diverse, and common advanced features with high return on investment (ROI) will be the main market entry points. The first areas to adopt AI in large numbers are likely to be those that require significant investment in physical safety, overall security, and risk prevention. Secondly, for industrial applications that can be quickly adapted to various uses with a little modification, the application of artificial intelligence is also a logical thing. There is no doubt that any organization should do research and develop a strategy on how AI can improve efficiency right now.