The pressing issue of carbon emissions has garnered attention globally because of the effects of urbanization and global warming on humanity. Data from the International Energy Agency reveals a staggering 15.6-fold increase in the world's annual carbon emissions over the 120-year period from 1900 to 2020 (Wang et al., 2023). Recognizing the urgency, countries advocate for a green and low-carbon economic development model to expedite the reduction of greenhouse gas emissions. Cities, being the focal point of human activity, are particularly vulnerable to climate change, making them pivotal in the strategy to diminish carbon emissions and address climate concerns. China's 14th Five-Year Plan concentrated efforts on building an ecological civilization, emphasizing carbon emissions reduction, pollution reduction, and the shift from quantitative to qualitative improvements in the ecological environment (Zhao et al., 2022). This strategic initiative aims to combat challenges posed by environmental pollution and achieve sustainable and high-quality economic development.

More than half of China's total national carbon dioxide emissions stem from thermal power generation, with lighting accounting for ~20% of this consumption and steadily rising to 40% over time (Petkovic et al., 2022). Aligned with the strategic goals of “carbon peak and carbon neutrality,” the imperatives of energy conservation, emission reduction, and green development underscore the need to curtail lighting power consumption. This reduction is pivotal in attaining the ambitious “double carbon” goal. High-power LEDs, emerging as the premier green light source of the new generation, are gradually replacing conventional lighting sources due to their attributes of low power consumption, high light efficiency, brightness, and a minimal carbon footprint (Unión-Sánchez et al., 2022). Their widespread applications in both indoor and outdoor settings (Chang et al., 2012) underscore their importance, with the lifespan of LED light sources emerging as a critical factor in energy economy and environmental sustainability (Padmasali and Kini, 2020). However, despite advancements in technology, conventional statistical techniques such as least squares, maximum likelihood estimation, and polynomials persist in the rated life prediction of LED light sources. This approach involves constructing a probability model based on the computation of a physical statistical model, utilizing probability distribution to characterize data regularities for life prediction and light source analysis. Nevertheless, this method is ill-suited for scenarios with high-dimensional and nonlinear features due to stringent distribution requirements and high standards for data quality. Maximum likelihood estimation, influenced by assumptions about data distribution and extreme values, adds complexity, especially when applying the least squares method for linear regression of luminous flux. Estimating regression parameters for polynomials is imperative, as unpredictable changes in random parameters result in extended prediction times, low model efficiency, and diminished prediction accuracy. Consequently, comprehensive testing of accelerated aging life, immunity to electromagnetic interference, weather resistance, and other factors directly impacting the life of light sources is essential before conducting an in-depth analysis of LED life.

An array of life prediction algorithms has emerged in recent years, primarily classified into two categories: traditional prediction models and machine learning prediction models, all aimed at achieving accurate and efficient life prediction of LED light sources (Illuminating Engineering Society, 2015). Within this landscape, traditional algorithms (Shi et al., 2020; Zhang J. et al., 2022; Zhang and Zhang, 2023) rely on physical and mathematical models to attain predictions, exhibiting low accuracy and a straightforward model structure. In response to the shortcomings of individual models, a common strategy involves the use of multi-parameter degradation models, traditional physical models (Cai et al., 2021; Cui et al., 2022), regression models (Kang et al., 2020), Markov chain (Song et al., 2021; Dvorzak et al., 2022), Bayesian networks (Dvorzak et al., 2022), and other random probability methods. However, these methods often entail lengthy prediction times, intricate networks, and low efficiency. Machine learning, on the other hand, leverages extensive training data, utilizing the gradient descent method to minimize the loss function and obtain optimal model parameters for life prediction. Examples include the adaptive neuro-fuzzy inference system (ANFIS) (Kyak et al., 2021), long short-term memory network (LSTM) (Pugalenthi et al., 2021; Zhang L. J. et al., 2023), LSTM and multi-representation domain adaptation (LSTM-MRAN) (Lyu et al., 2022), width-based learning system and LSTM (B-LSTM) (Wang et al., 2022), multi-dimensional deep neural network (MDNN) (Yang et al., 2022), multi-neural network fusion (Kumari et al., 2021; Ma and Mao, 2021; Hu et al., 2023). In the realm of LED life prediction applications, machine learning stands out for its swift forecasting times, superior accuracy, and efficiency. Nonetheless, the machine learning network is not without its drawbacks, encompassing extended training times and over-computation, where the quality of training data and network architecture significantly impacts prediction accuracy.

In conclusion, the future trajectory points toward realizing LED light source life forecasting through the application of machine learning fusion algorithms. Much of the current literature focuses on LED packaging and drive life forecasting, facing challenges in data collection, high prediction costs, and infrequent prediction times due to excessive precision in considering influencing elements. Presently, the examined prediction techniques can materialize predictions derived from machine learning and statistical model dimensions, albeit with limitations in accuracy, generalization, and adaptation. To reduce the cost of light source life prediction, enhance precision and real-time functionality, and establish logical relationships between various algorithms, this work commences with an exploration of light decay impacting factors. Subsequently, the introduction covers light attenuation, reliability, and accelerated life models. The following section reviews conventional algorithms, Bayesian networks, and artificial neural network algorithms, emphasizing the development trend, particularly focusing on deep learning and model fusion algorithms. The final section encapsulates the advantages and drawbacks of various algorithms while outlining prospects for the implementation of quick, cost-effective, and highly accurate prediction algorithms from the perspectives of manufacturers, governments, and researchers.

The theoretical lifespan of an LED light source can extend up to one hundred thousand hours, yet the practical application life merely encompasses 30%-50% of this theoretical value. Examining the index analysis of light source reliability is crucial for conserving lighting power, mitigating environmental pollution and light pollution, and advancing the development of a national low-carbon economy (Pang and Cai, 2021). The light source life serves as a pivotal technical parameter for evaluating the reliability and quality of a light source, necessitating research into life prediction (Lee and Kim, 2021). Accelerated aging life tests demonstrate that factors such as temperature, humidity, and chemical corrosion induce light decay (Feuk et al., 2021), impacting the overall service life of the light source. Therefore, this chapter initiates by elucidating the influence of LED device materials on the light source's lifespan, delving into the characteristics of different devices, and encapsulating the future development trends. Ultimately, a mathematical model is formulated based on the principles of light source decay, establishing a robust theoretical foundation for subsequent life prediction algorithms.

The ability of a product to perform its designated functions within specified conditions and timeframes is termed reliability (Li F. et al., 2020). With each alteration to the required parameters, reliability diminishes until the product's performance reaches a point of failure. Failures can be categorized as either destructive or parameter-based (Liu W.-G. et al., 2020). Destructive failure leads to the product ceasing to function due to various causes, while parameter failure involves a decline in the product's performance index but continued operation. This study investigated parameter failure, specifically from the perspective of luminous flux L70 and light attenuation.

The conventional TM-21 regression technique falls short of adequately describing the rapid failure of light sources, and the duration and accuracy of predictions cannot meet present demands. Unreasonable modeling and parameter estimates pose challenges for recent artificial intelligence-based prediction techniques. Divergent machine learning algorithms grapple with inadequate training data, resulting in significant prediction variations and poor accuracy. In summary, the application of machine learning algorithms to LED light source life prediction technology is undoubtedly the future development trend, given their broad applicability and rapid evolution. Potential future development directions include:

Researchers are advised to undertake thorough investigations into light source life prediction, approaching the subject from multiple angles. This effort aims to propel the green lighting initiative forward and elevate the benchmarks for urban green, low-carbon lighting construction, and intelligent control.

The creation of an influencing factor model involves determining the contribution of various factors to prediction outcomes. This is achieved through a thorough investigation of key drivers, luminaires, connecting components, and other relevant elements affecting the luminosity degradation of LED light sources. Employing conventional physics and statistical regression procedures will markedly enhance the accuracy and prediction algorithm speed of life prediction technology. This, in turn, will guide the rapidly advancing field of LED production technology, fostering a positive feedback loop.

Despite the superior efficiency and accuracy demonstrated by BPNN, LSTM, ANFIS, and MDNN over earlier algorithms, there remains a disparity in people's expectations regarding prediction speed and accuracy. In terms of both accuracy and efficiency, the model fusion method significantly outperforms the single-life prediction approach. This method has the capability to reduce the time required for processing nonlinear and intricate data samples, thereby enhancing prediction accuracy.

Production companies must intensify efforts in advancing green, energy-saving technologies and developing intelligent lighting products aligned with the principles of “green lighting.” This entails a commitment to high efficiency, energy conservation, environmental sustainability, safety, and comfort. Companies should actively contribute to urban intelligent construction while gradually phasing out outdated production capacities. The integration of state-of-the-art machinery, the Internet of Things, and big data technology facilitates a comprehensive assessment of light source performance, enhancing efficiency, and reducing raw material energy consumption. This sets the stage for the creation of green lighting and the development of intelligent cities.

The government plays a pivotal role in achieving the “double carbon” goal, accelerating the low-carbon transformation of energy consumption, and attaining the national strategic objective of “carbon peak, carbon neutrality.” Utilizing life forecast results from LED light sources as theoretical guidance, the government can formulate energy-saving and carbon-reducing lighting regulations. These policies can drive various industries toward carbon neutrality, laying the groundwork for the industrial sector's carbon peak by 2030. Currently, the durability of an LED is influenced by various factors such as drivers, packaging, heat dissipation treatment, and lead welding techniques. Variability in product quality among manufacturers significantly impacts the progress of low-carbon lighting technology. To achieve the “dual-carbon” goal and ensure high-quality, sustainable growth in the lighting industry, the government should establish a green, energy-saving, and low-carbon light source manufacturing standard system in the lighting sector. It should actively support energy conservation and emission reduction across the entire value chain, guiding the lighting industry toward a circular economy model.

In conclusion, challenges in the current life prediction model algorithm include difficult data collection, overly specific influencing elements, expensive prediction costs, and low prediction frequency. The statistical model and machine learning dimensions predominantly contribute to the model's low accuracy, limited generalization capacity, and inadequate adaptability. Therefore, to enhance feature expression by considering time and channel dimensions, the future model should integrate the influencing factor decomposition model and the principal component analysis method to screen significant driving factors as model input. Additionally, combining traditional and deep learning algorithms will minimize model complexity and enhance generalizability. This approach provides technical support for fostering the green economy and optimizing the country's industrial structure.

GS: Writing – original draft, Methodology, Investigation, Conceptualization. YB: Writing – review & editing, Supervision. ZZ: Writing – review & editing, Conceptualization.