Smart Library: AI Enhance Visitors Comfort and Books Preservation via Temperature and Humidity Control

Libraries have long been recognized as pillars of knowledge and information, offering resources that fuel learning, research, and intellectual exploration. With the advent of the digital age, libraries have adapted by incorporating technology to remain relevant and efficient. However, one constant remains: the need to preserve the physical collections of books, manuscripts, and archival materials that they house. The conservation of these valuable resources is deeply influenced by environmental factors such as temperature and humidity. Thus, managing these conditions becomes crucial to both the preservation of the materials and the comfort of library visitors. While libraries serve as sanctuaries of knowledge, they must also provide environments that are conducive to study and research. Therefore, libraries face a dual challenge: ensuring visitor comfort and protecting their collections from deterioration due to environmental factors. Environmental conditions, particularly temperature and humidity, play a significant role in the long-term preservation of library materials. Paper, the primary medium for books, is sensitive to fluctuations in both temperature and humidity, which can accelerate its degradation over time. According to preservation guidelines, the ideal humidity for storing library materials is between 40-60%, with a temperature range of 20-24°C(Andriyani et al., 2021). These conditions help prevent the growth of mold, the drying out of paper fibers, and the warping of book covers, all of which can occur if these parameters are not maintained(DPMG Kota Banda Aceh, 2010; Hasibuan, 2022). However, achieving and maintaining these optimal environmental conditions in libraries is not a trivial task, especially in larger facilities where the physical structure, seasonal climate changes, and visitor traffic can affect indoor conditions. Libraries, therefore, must find ways to strike a balance between preservation needs and visitor comfort, and this is where the advent of artificial intelligence (AI) comes into play(Cao et al., 2018). Recent advancements in AI technology have introduced new possibilities for optimizing environmental control systems in libraries. AI-driven solutions such as intelligent thermostats can autonomously regulate temperature and humidity levels, ensuring that they remain within the ideal range for both book preservation and visitor comfort. These systems are capable of learning and adapting to changes in the environment by analysing historical data and real-time inputs from Internet of Things (IoT) sensors embedded throughout the library. Intelligent thermostats, for example, can detect when visitor numbers rise or fall and adjust environmental controls accordingly to maintain a consistent atmosphere(Lazim & Hidayat, 2022; Shahzad et al., 2024; Xie et al., 2019). Such innovations eliminate the need for manual adjustments, which can be prone to human error, and provide a level of precision that was previously unattainable. The potential of AI to enhance library management is significant. Not only can AI systems optimize the control of environmental factors, but they can also integrate with other library systems to provide a holistic management approach. IoT sensors can continuously monitor environmental conditions and automatically report them to a centralized database, where AI algorithms can analyse the data to predict future conditions and recommend adjustments in real-time(Ciampi et al., 2024; Gonzalo et al., 2022; Long, 2023; Ogundiran et al., 2024; Rovelli et al., 2014; Schiavon & Lee, 2013; Taheri et al., 2020). This integration allows for a dynamic and proactive approach to environmental control, addressing problems before they occur and ensuring that both visitor comfort and book preservation are maintained at optimal levels. Furthermore, by analysing long-term trends, AI systems can help libraries better understand how environmental factors fluctuate over time, enabling more informed decision-making about maintenance, energy use, and preservation strategies. Despite the promise that AI holds for improving environmental control in libraries, there has been limited research specifically focused on how these technologies can directly impact the preservation of book collections and visitor experience. Most of the existing studies focus on general applications of AI in energy efficiency and climate control within buildings but do not address the unique requirements of libraries, where rare and valuable materials must be preserved under specific conditions. As such, there is a gap in the literature regarding the practical implications of AI in this domain. This study aims to bridge that gap by examining the role of AI in temperature and humidity regulation within libraries and its effects on both visitor comfort and the conservation of valuable book collections. In conducting this research, a key focus will be on evaluating the ways in which AI-driven environmental control systems can contribute to enhancing the quality of the library experience for visitors. Libraries are not just repositories of books; they are also spaces where students, researchers, and the public come to study, collaborate, and engage with knowledge(Andriyani et al., 2021; Cao et al., 2018; Gandini, 2019; Gul & Bano, 2019; Hartono, 2017; Muliyadi, 2013; Shahzad et al., 2024; Surachman, 2016; Taheri et al., 2020; Yuan & Yang, 2023). As such, the physical environment within a library plays a critical role in shaping the visitor experience. Poorly managed temperature and humidity levels can create discomfort, leading to decreased productivity and enjoyment for visitors. By contrast, an environment that is well regulated can encourage extended visits and foster a more productive learning atmosphere. Therefore, it is essential to explore how AI technology can be leveraged to create more comfortable and welcoming library environments. In addition to improving visitor comfort, this research will investigate how AI systems can enhance the preservation of rare and valuable book collections. Books, particularly those that are old or made of delicate materials, are highly susceptible to damage from environmental factors. When temperature or humidity levels deviate from the recommended range, books can suffer from physical deterioration, such as warping, mold growth, or fading of ink. Over time, this can lead to the irreversible loss of important cultural and historical materials. By implementing AI-driven environmental control systems, libraries can better protect their collections by ensuring that temperature and humidity remain consistently within the safe range. This not only extends the lifespan of books but also reduces the need for costly restoration efforts and ensures that future generations can access these invaluable resources. Furthermore, AI technology can contribute to the development of more sustainable and energy-efficient library operations. Traditional climate control systems often operate on fixed schedules or rely on manual input, which can result in inefficient energy use and increased operational costs. AI systems, on the other hand, can optimize energy consumption by adjusting heating, ventilation, and air conditioning (HVAC) systems based on real-time data and predictive algorithms(Bi et al., 2024). This allows libraries to maintain ideal environmental conditions while minimizing energy waste, contributing to both cost savings and environmental sustainability. In a world increasingly focused on reducing carbon footprints and promoting green technologies, the adoption of AI in libraries can serve as a model for other institutions seeking to balance preservation with sustainability. The broader implications of this study extend beyond the library environment. The findings could provide valuable insights into the development of AI systems tailored to the specific needs of cultural institutions, such as museums, archives, and galleries, which also face similar challenges in preserving sensitive materials. By demonstrating the effectiveness of AI in optimizing environmental controls for the conservation of books, this research could pave the way for the implementation of similar technologies in other cultural settings, thereby contributing to the preservation of global heritage. Moreover, the study could inform policymakers and library administrators about the benefits of investing in AI technologies, not only from a preservation standpoint but also in terms of improving visitor experience and operational efficiency(Abrianto et al., 2021; Andriyani et al., 2021; Anggoro & Hidayat, 2020; Arifin et al., 2017; Astari et al., 2023; Awaluddin et al., 2022; Tyas & Sumiharto, 2013). Another significant aspect of this research is the potential for AI systems to support decision-making processes within library management. By analyzing data collected from environmental sensors over time, AI algorithms can provide insights into patterns and trends that may not be immediately apparent to human operators. For example, AI systems could detect correlations between environmental conditions and visitor behaviors, such as increased foot traffic during certain times of the day or seasonal variations in humidity levels that affect book preservation. These insights could inform future library policies and strategies, enabling administrators to make more informed decisions about resource allocation, maintenance schedules, and investment in new technologies(Gallardo et al., 2016). Additionally, this research will explore the potential of AI to contribute to the long-term conservation of cultural heritage through the preservation of books and other written materials. Libraries play a crucial role in safeguarding the knowledge and history contained within their collections, and the implementation of AI technology can enhance their ability to fulfill this responsibility. By ensuring that environmental conditions are consistently maintained at optimal levels, AI systems can prevent the gradual deterioration of books, ensuring that they remain accessible to future generations. This is particularly important for rare and valuable items, which are often irreplaceable and hold significant historical, cultural, or academic value. The implications of this study are far-reaching, touching on multiple aspects of library management and conservation. From improving visitor comfort to enhancing the preservation of rare books, AI has the potential to revolutionize the way libraries operate. Furthermore, the findings from this research could serve as a foundation for developing new policies and strategies that promote the adoption of AI technologies in libraries and other cultural institutions. By demonstrating the tangible benefits of AI in optimizing environmental control systems, this study could encourage more libraries to invest in these technologies, leading to more sustainable and efficient operations. In conclusion, the application of AI technology in libraries offers exciting possibilities for both improving visitor experiences and preserving valuable book collections. Through intelligent environmental control systems, libraries can maintain the delicate balance between comfort and conservation, ensuring that they remain welcoming spaces for learning and research while safeguarding the materials that form the backbone of human knowledge. This research seeks to provide a comprehensive analysis of how AI can be harnessed to achieve these goals, filling a critical gap in the literature and offering practical insights for libraries looking to innovate in the digital age. Libraries have long been recognized as vital institutions in the preservation and dissemination of knowledge. They serve not only as repositories of books but as living archives that facilitate learning, research, and intellectual development. As the custodians of knowledge, libraries play an integral role in supporting academic institutions, research initiatives, and the general public’s pursuit of lifelong learning. The maintenance of a library’s collection, however, goes far beyond mere storage. It demands a nuanced understanding of the environmental conditions that ensure the long-term preservation of books and materials while simultaneously ensuring the comfort of library visitors(Purwani, 2021; Suhardi, 2011). One key factor that influences both visitor comfort and the preservation of library collections is the management of temperature and humidity within the library environment. Research consistently highlights the delicate balance required to maintain optimal environmental conditions for books and other archival materials. According to conservation guidelines, the ideal humidity for preserving library materials falls within the range of 40-60%, while the recommended temperature is between 20-24°C. Deviations from these conditions can accelerate the deterioration of paper, ink, and binding materials, leading to irreversible damage over time. For example, excessive moisture can lead to the growth of mold or mildew on paper, while extreme dryness can cause paper to become brittle and fragile. Temperature fluctuations can also contribute to the weakening of book bindings and warping of paper. Thus, the control of environmental conditions within libraries is not merely a matter of comfort but one of preservation. The stakes are particularly high in institutions that house rare or fragile materials, where improper conditions can result in the loss of invaluable cultural heritage. In this context, achieving precise and stable control over temperature and humidity is a pressing concern for library administrators. Traditionally, climate control systems in libraries have relied on centralized HVAC (Heating, Ventilation, and Air Conditioning) systems. These systems, while effective to a degree, are not without limitations. They can be energy intensive, costly to maintain, and may not offer the level of precision required to meet the stringent demands of preservation efforts. In recent years, however, advancements in artificial intelligence (AI) have opened new avenues for optimizing climate control within libraries. AI technology has already made significant inroads in various industries, from healthcare to manufacturing, and its application in the realm of library management is beginning to take shape. One promising development is the integration of AI-driven systems with traditional climate control technologies. Intelligent thermostats, for example, use AI algorithms to monitor and regulate temperature and humidity levels automatically, adjusting settings in real-time based on environmental data and usage patterns. These systems not only ensure a more stable and precise climate within the library but also have the potential to reduce energy consumption by optimizing when and how HVAC systems are used. Several case studies have demonstrated the effectiveness of AI-enabled thermostats in maintaining optimal environmental conditions. These systems are capable of learning the typical patterns of library usage and adjusting settings accordingly, ensuring that the climate remains ideal for both visitors and the collection. By integrating these systems with Internet of Things (IoT) technologies, libraries can further enhance their climate control capabilities. IoT sensors placed throughout the library can continuously monitor conditions in real time, providing data that AI algorithms use to make instantaneous adjustments to temperature and humidity levels. Additionally, the data collected by these systems can be stored in centralized databases, enabling long-term analysis of environmental trends and helping library staff make informed decisions about maintenance and upgrades. While the application of AI in climate control offers clear benefits, there remains a notable gap in the literature regarding its specific impact on library environments. While several studies have explored the use of AI in optimizing energy usage or enhancing visitor comfort, few have examined how AI-driven temperature and humidity adjustments directly affect the preservation of library collections. This is a significant oversight, as the preservation of books, manuscripts, and other cultural artifacts is one of the primary responsibilities of libraries, particularly those that house rare or irreplaceable materials. The aim of this study is to fill that gap by exploring the role of AI technology in improving the preservation of library collections through more precise environmental control. Specifically, this research will examine how AI-enabled climate control systems can help maintain the optimal conditions necessary for preserving paper based materials, as well as their broader implications for the visitor experience. By focusing on the intersection of AI and library preservation, this study seeks to provide a comprehensive analysis of how emerging technologies can enhance the quality of library services and protect cultural heritage for future generations. The implications of AI technology in this domain extend beyond the technical aspects of climate control. One of the primary goals of any library is to create a welcoming and conducive environment for its visitors. For students, researchers, and casual readers alike, comfort plays a significant role in their ability to focus, engage, and make the most of their time in the library. A well-regulated climate can make the difference between a productive study session and a frustrating experience, especially in regions where temperature and humidity levels outside the library can vary significantly. By automating the adjustment of environmental conditions, AI-driven systems free up library staff to focus on other essential duties, ultimately improving the overall visitor experience. Moreover, the potential for AI to revolutionize climate control in libraries aligns with broader trends in sustainability and energy efficiency. Libraries, like other public institutions, face increasing pressure to reduce their carbon footprint and implement environmentally friendly practices. Traditional HVAC systems are notorious for their high energy consumption, particularly in large, multi-story libraries that must maintain stable conditions across various rooms and sections. AI-enabled systems, by contrast, can optimize energy usage by making micro-adjustments in real time, reducing the need for continuous operation of heating and cooling units. This not only lowers energy costs but also contributes to a library’s sustainability goals. Over time, the savings generated from reduced energy consumption could be reinvested into other areas of library operations, such as expanding digital archives or upgrading research facilities. In addition to improving energy efficiency, AI systems could also contribute to more precise conservation efforts. By leveraging the vast amounts of data generated by IoT sensors and AI algorithms, libraries can gain a deeper understanding of how environmental conditions fluctuate over time. This information can then be used to develop more tailored preservation strategies. For example, AI systems could be programmed to recognize patterns that precede damaging environmental shifts, such as a sudden increase in humidity during the rainy season, and take proactive measures to mitigate the risk. In the long term, these predictive capabilities could prevent costly damage to collections, ensuring that rare and valuable materials remain intact for future generations. The research conducted in this study will also provide a basis for the development of AI systems specifically designed to meet the needs of libraries. While general-purpose AI climate control systems have shown promise, there is a need for solutions tailored to the unique requirements of library environments. Libraries, after all, are not just large buildings but complex ecosystems that house a variety of materials, each with its own preservation needs. Rare manuscripts, historical documents, modern printed books, and digital media all require different environmental conditions to ensure their longevity. The next generation of AI systems will need to be flexible and adaptable, capable of adjusting settings not just based on temperature and humidity data but also on the specific preservation needs of different collections within the library. The findings from this study could inform the development of such systems, providing valuable insights into the specific challenges that libraries face in maintaining optimal environmental conditions. Moreover, these insights could serve as the foundation for future collaborations between libraries, AI developers, and conservation experts. Together, these stakeholders can work to create AI-driven climate control solutions that are not only effective in terms of preservation but also user-friendly and cost-efficient. The broader implications of this research are equally significant. By contributing to the development of more sustainable and efficient library management practices, AI technology has the potential to play a key role in the conservation of cultural heritage. Libraries are not just repositories of knowledge; they are stewards of history, culture, and the collective memory of society. The preservation of their collections is, therefore, a matter of public interest. As AI technology continues to evolve, its applications in the realm of library management could help ensure that future generations have access to the wealth of knowledge and cultural artifacts housed within these institutions. In conclusion, the integration of AI technology into library management, particularly in the domain of climate control, represents a promising avenue for both improving visitor comfort and enhancing the preservation of library materials. This research aims to provide a comprehensive analysis of how AI-driven systems can contribute to these goals, offering new insights into the potential of AI to revolutionize library management. By focusing on the intersection of AI, sustainability, and cultural heritage preservation, this study seeks to lay the groundwork for future innovations that will ensure the continued relevance and vitality of libraries in the digital age

Research by (Bi et al., 2024, p. 2) highlights the advantages of artificial intelligence-based fault detection and diagnosis (FDD) methods in managing HVAC systems within library environments. Libraries, with their strict environmental requirements for both human comfort and the preservation of valuable materials, stand to benefit significantly from the enhanced operational efficiency and reduced system downtime offered by AI-driven solutions. The study, which analysed literature from 2013 to 2023, shows that AI methods, including machine learning, deep learning, and hybrid models, outperform traditional physical model-based techniques in terms of accuracy. Additionally, these AI systems reduce the dependency on expert knowledge, making them more accessible for wider application. The ability to maintain a stable indoor climate is critical for libraries, as fluctuations in temperature and humidity can cause irreversible physical damage to books and archival materials. Furthermore, the adaptation of library environments to better accommodate visitor comfort leads to an enhanced learning atmosphere, fostering academic engagement. AI-based FDD methods provide libraries with tools to ensure that HVAC systems maintain optimal conditions. Ideal temperature and humidity levels are not only vital for material preservation but also contribute to the overall comfort of library patrons, ensuring a productive environment. However, these methods are not without challenges. Dynamic environmental changes and fluctuating time conditions pose obstacles to FDD resolution accuracy, necessitating ongoing refinement of AI algorithms. Addressing these challenges is crucial to expanding the adoption and effectiveness of AI-based HVAC systems in libraries. Ciampi et al. (2024, p. 9) further explore the connection between HVAC system optimization and both material preservation and visitor comfort in libraries. Bayesian Networks (BN) have been identified as a useful tool for predicting energy consumption in HVAC systems, which can help maintain the ideal environmental conditions necessary for the preservation of books and other materials. Precise predictions allow for the fine-tuning of HVAC systems to achieve optimal temperature and humidity levels, which in turn reduce energy consumption and lower operational costs. This not only benefits libraries economically but also contribute to environmental sustainability. However, the limitations of BN in this context cannot be ignored. The complexity of modelling and interpreting the probabilistic relationships between numerous variables presents a significant challenge in the design and implementation of BN models. Additionally, BN requires large amounts of high-quality data to produce accurate predictions. The performance of BN models is highly dependent on the data available, and any deficiencies can lead to inaccuracies. The need to discretize continuous variables also introduces potential errors, further compromising the accuracy of the predictions. While BN provides valuable probabilistic insights, it may fall short when compared to more advanced machine learning techniques, which are better equipped to capture the nonlinear relationships inherent in HVAC systems. As a result, researchers and practitioners must weigh the strengths and weaknesses of BN carefully before deciding on its application in HVAC energy consumption analysis. Further development or integration with other methods may be necessary to improve the reliability of future predictions. Long (2023) research discusses the significant costs involved in constructing a building envelope designed to optimize energy efficiency. These costs are particularly high when retrofitting an existing building, making it more practical to incorporate energy-efficient design elements at the early stages of construction. This finding is especially relevant for libraries, where long-term operational savings from energy efficient design can offset the initial investment. Ogundiran et al. (2024) focus on AI’s potential in optimizing HVAC settings for thermal comfort and indoor air quality (IAQ) in libraries. AI-driven systems can analyze data from temperature, humidity, and air quality sensors to adjust HVAC settings in real time. By predicting energy consumption patterns based on historical data, AI enables the development of energy-saving strategies that reduce waste without sacrificing environmental quality. Furthermore, AI algorithms can detect anomalies in HVAC systems, such as spikes in energy consumption or mechanical failures, allowing for immediate corrective action. Through predictive modeling of indoor temperature and other factors, AI helps to maintain thermal comfort while minimizing energy usage. The automation of HVAC controls based on real-time needs significantly reduces the need for manual intervention, further improving operational efficiency and system reliability. Together, these studies underscore the transformative potential of AI in HVAC management for libraries, where maintaining an optimal environment is crucial for both the preservation of collections and the comfort of patrons. However, the path toward fully optimized AI-driven systems involves navigating technical challenges and cost considerations, particularly in the adoption of advanced models like Bayesian Networks. With further research and development, AI has the potential to revolutionize how libraries manage their environmental systems, ensuring sustainability, efficiency, and comfort. Research by (Bi et al., 2024) shows that AI-based fault detection and diagnosis (FDD) methods offer significant advantages over physical model-based approaches in managing HVAC systems within libraries. Libraries, which require stable environmental conditions for user comfort and book preservation, benefit from the precision and efficiency of AI-driven systems. These methods, including machine learning, deep learning, and hybrid AI models, ensure better accuracy while reducing reliance on expert intervention. By maintaining an optimal indoor climate, AI helps prevent damage to collections and creates a comfortable environment conducive to learning. However, challenges such as environmental instability and time-sensitive conditions still pose hurdles to wider AI adoption. (Ciampi et al., 2024) highlight that Bayesian Networks (BN) can be useful for energy consumption prediction in libraries, helping optimize HVAC settings for ideal temperature and humidity. This optimization benefits both the preservation of book materials and visitor comfort. However, BN's complexity, heavy data requirements, and difficulty handling continuous variables limit its efficiency in all scenarios. While BN offers probabilistic insights, it may be less effective than more advanced machine learning models in capturing nonlinear dynamics in HVAC systems. Further research or hybrid methods may be necessary to enhance its predictive accuracy. Additionally, (Long, 2023) notes that constructing efficient building envelopes is cost-intensive, best undertaken during the initial construction of library buildings. Meanwhile, Ogundiran et al. (2024) emphasize that AI can optimize thermal comfort and indoor air quality by leveraging data from sensors and predicting energy consumption patterns. AI's ability to detect anomalies, automate HVAC controls, and adjust settings in real-time improves energy efficiency, reduces manual intervention, and enhances overall operational performance.

The integration of AI into library environmental management systems offers significant potential to improve operational efficiency and preserve valuable collections through increased accuracy in predicting and controlling environmental conditions compared to traditional methods. Nonetheless, challenges such as high implementation costs, complex AI models, and the need for extensive data remain. A phased approach, starting with AI-based fault detection and diagnosis (FDD) systems, can improve HVAC performance and reliability, followed by more sophisticated predictive models to optimize energy consumption. In new construction or major renovation projects, AI-driven building envelope optimization should be considered. Research on real-world applications of AI in libraries is essential, especially in addressing challenges related to human comfort and book preservation, with standardized sensor placement and data collection methods being key to developing robust AI systems. AI-based FDD methods in HVAC systems offer advantages such as improved operational efficiency and reduced system downtime, with machine learning techniques showing high accuracy in controlling temperature and humidity. However, methods such as Bayesian Networks (BN), while offering valuable probabilistic insights, often struggle with the complexity of HVAC modeling and conditionality.