Integrating AI and Environmental Analytics for Enhanced Productivity and Safety

International Journal of Environmental Research and Public Health

Existing heat–health warning systems focus on warning vulnerable groups in order to reduce mortality. However, human health and performance are affected at much lower environmental heat strain levels than those directly associated with higher mortality. Moreover, workers are at elevated health risks when exposed to prolonged heat. This study describes the multilingual “HEAT-SHIELD occupational warning system” platform (https://heatshield.zonalab.it/) operating for Europe and developed within the framework of the HEAT-SHIELD project. This system is based on probabilistic medium-range forecasts calibrated on approximately 1800 meteorological stations in Europe and provides the ensemble forecast of the daily maximum heat stress. The platform provides a non-customized output represented by a map showing the weekly maximum probability of exceeding a specific heat stress condition, for each of the four upcoming weeks. Customized output allows the forecast of the personalized local heat-st...

Urban Climate, 2024

In the face of global climate warming, outdoor thermal comfort in urban settings is increasingly critical. However, accurately predicting residents' thermal perceptions during outdoor activities remains challenging due to complex environmental dynamics. This study introduces a human-centered digital twin framework that integrates physiological data, atmospheric conditions, and urban building environment features, with multiple machine learning models employed to predict and analyze outdoor thermal comfort in different regions of Singapore. Among these methods, the Bayesian-tuned XGBoost model exhibits the highest accuracy (0.66), notably excelling in categorizing “Prefer cooler” and “Prefer no change” responses. SHAP value analysis identifies key influencing factors such as human activity intensity (heart rate), geographical location (longitude and latitude), meteorological conditions (solar azimuth angle, dew point temperature), and greenery (Normalized Difference Vegetation Index). Based on the most effective machine learning method, this research develops a user-personalized real-time prediction model for urban thermal comfort perception. The extensive hourly grid-based prediction results illustrate the spatiotemporal variations in outdoor thermal comfort, highlighting preference differences across locations, seasons, and activity levels. Results underscore the efficacy of the human-centric digital twin approach and machine learning in managing urban thermal environments, leveraging multi-source data to complement traditional survey methods effectively.

Building Simulation Conference proceedings

When managing the energy performance of a portfolio of buildings over time, climate change can be a threat as it can cause significant changes in energy use patterns. This paper uses artificial intelligence techniques to develop an AI-based forecasting tool, Campus Energy Use Prediction (CEUP) that can help managers to forecast campus future monthly energy use under various climate scenarios. We have leveraged historical energy use data of buildings in the University of Florida, Gainesville, FL to develop CEUP. CEUP was then used to forecast the impact of climate change with the average outdoor temperature of the median, hottest, and coldest years of future climate scenarios of Gainesville, FL as input.

Environmental Chemistry Letters, 2023

Climate change is a major threat already causing system damage to urban and natural systems, and inducing global economic losses of over $500 billion. These issues may be partly solved by artificial intelligence because artificial intelligence integrates internet resources to make prompt suggestions based on accurate climate change predictions. Here we review recent research and applications of artificial intelligence in mitigating the adverse effects of climate change, with a focus on energy efficiency, carbon sequestration and storage, weather and renewable energy forecasting, grid management, building design, transportation, precision agriculture, industrial processes, reducing deforestation, and resilient cities. We found that enhancing energy efficiency can significantly contribute to reducing the impact of climate change. Smart manufacturing can reduce energy consumption, waste, and carbon emissions by 30-50% and, in particular, can reduce energy consumption in buildings by 30-50%. About 70% of the global natural gas industry utilizes artificial intelligence technologies to enhance the accuracy and reliability of weather forecasts. Combining smart grids with artificial intelligence can optimize the efficiency of power systems, thereby reducing electricity bills by 10-20%. Intelligent transportation systems can reduce carbon dioxide emissions by approximately 60%. Moreover, the management of natural resources and the design of resilient cities through the application of artificial intelligence can further promote sustainability.

Proceeding of the 33rd European Safety and Reliability Conference

Nowadays, there has been considerable research regarding the public health and environmental aspects of Climate Change, but the literature on the potential impacts of Climate Change on the health and safety of outdoor workers has received limited attention. Outdoor workers which include, by a way of example, agricultural, construction, and transportation workers, and other workers exposed to outdoor weather conditions, are exposed at increased risk of heat stress and other heat-related ailments, extreme weather, and occupational injuries due to Climate Changerelated issues. Climate Change is increasing environmental temperatures and extreme weather events, affecting air pollution and the distribution of pesticides and pathogens. The implementation of enhanced occupational health and safety measures that can cope with the effects of Climate Change on workers is a key step towards the adaptation perspective that must be embraced to ensure a safer and more sustainable future for the workers. In this paper, a new tool named Climate Change -House of Safety (CC-HoS) is designed to address new risks and to carry out in an effective way the risk assessment considering specifically the risks related to Climate Change. The CC-HoS, derived from the House of Safety (HoS), aims to investigate the direct (i.e., warming, extreme weather, ...) and indirect impacts (i.e., air pollution, UV exposure, vector-born disease, ...) of Climate Change on workers' health and safety in outdoor worksites. This tool can correctly identify and assess risks through the Risk Priority Number (RPN) in terms of Severity, Detectability, and Occurrence criteria, while determining the most suitable safety devices and preventive/protective measures to manage the previously identified risks. The proposed approach is applied to a company operating in the agricultural sector. The effectiveness and usefulness of the tool for selecting the most effective technical solutions to mitigate risks related to Climate Change are presented in the case study.

International Journal of Advances in Scientific Research and Engineering, 2025

In response to the growing challenges posed by climate change, there is an urgent need for innovative solutions that leverage data insights to enhance climate resilience and sustainability. This paper presents the design and development of Climate IQ Smart Solutions, a comprehensive system that harnesses advanced data analytics, machine learning, and Internet of Things (IoT) technologies to provide actionable insights for various stakeholders, including farmers, city planners, and environmental researchers. Climate IQ Smart Solutions aims to empower users by delivering precise, real-time information and predictive analytics to optimize resource management, mitigate adverse climate impacts, and promote sustainable practices. The system integrates diverse data sources, including IoT sensors, satellite imagery, and historical climate data, into a unified platform. This data is processed using state-of-the-art cloud infrastructure and advanced machine-learning algorithms to generate valuable insights. The architecture of ClimateIQ Smart Solutions comprises several core components: data acquisition, data storage, data processing, and user interface. Data acquisition involves collecting real-time data from a network of IoT sensors and external data sources. This data is then stored in a scalable cloud-based storage system, ensuring efficient handling of large volumes of information. Advanced data processing techniques, including machine learning and predictive analytics, are employed to analyze the data and extract meaningful patterns and trends. Finally, an intuitive user interface presents these insights in a user-friendly manner, allowing stakeholders to make informed decisions. Key features of Climate IQ Smart Solutions include real-time monitoring of environmental conditions, predictive analytics for forecasting climate-related events, and recommendations for optimizing resource usage. For instance, farmers can use the system to monitor soil moisture levels and receive irrigation recommendations, while city planners can leverage predictive models to prepare for extreme weather events.

2020

Controlling and forecasting environmental variables (e.g., air temperature) is usually a key and complex part in a greenhouse management architecture. Indeed, a greenhouse inner micro-climate, which is the result of an extensive set of interrelated environmental variables influenced by external weather conditions, has to be tightly monitored, regulated, and, sometimes, forecast. Nowadays, Wireless Sensor Networks (WSNs) and Machine Learning (ML) are two of the most successful technologies to deal with this challenge. In this paper, we discuss how a Smart Gateway (GW), acting as a collector for sensor data coming from a WSN installed in a greenhouse, could be enriched with a Neural Network (NN)-based prediction model allowing to forecast a greenhouse's inner air temperature. In the case of missing sensor data coming from the WSN, the proposed prediction algorithm, fed with meteorological open data (gathered from the DarkSky repository), is run on the GW in order to predict the missing values. Despite the model is especially designed to be lightweight and executable by a device with constrained capabilities, it can be adopted either at Cloud or at GW level to forecast future air temperature's values, in order to support the management of a greenhouse. Experimental results show that the NN-based prediction algorithm can forecast greenhouse air temperature with a Root Mean Square Error (RMSE) of 1.50 °C, a Mean Absolute Percentage Error (MAPE) of 4.91%, and a R 2 score of 0.965.

Building and Environment, 2022

Highlights •Three-stage air conditioning control process reduces carbon emissions. •Three-stage air conditioning control process saves electricity. •Air conditioning temperature controlling using weather forecasts saves energy. Abstract Objective To explore whether a climate services approach can enable both energy saving and indoor comfort. Methods After analysis of the local weather forecast, the air conditioning temperature of a government building was set to be adjusted at 7:30, 11:00, and 17:00. The indoor temperature and humidity were measured by using a MAPS6.0 sensor, and thermal comfort was calculated using ISO 7730 standards. Multiple regression analysis was used to examine the correlation between the weather forecast and chiller energy consumption. Results After a three-stage air conditioning temperature control process was implemented, predicted mean vote values between 0 and 0.5 were achieved for 79.1% of the working hours in the experimental period. Outdoor and air conditioning temperatures were the most critical factors affecting the hourly energy consumption of the air conditioner and water-cooled chiller. With every 1 °C increase in the outdoor temperature, the hourly energy consumption increased by 6.57. Moreover, with every 1 °C increase in the air conditioner temperature, the hourly energy consumption decreased by 8.63. Discussion A predicted mean vote value of >0.5 was recorded for 15.9% of the working hours; this may be related to high indoor humidity, weather forecast inaccuracies, and the air conditioner being turned on too late in the day. From July to October 2021, 8749 kW h of electricity were saved, and carbon emissions were reduced by 1405 kg. Regulating air conditioning temperatures through weather forecasts can achieve energy savings. Keywords Weather forecast data, Climate services, Energy savings, ASHRAE 55, Predicted mean vote

Sensors

Air pollution has become the most important issue concerning human evolution in the last century, as the levels of toxic gases and particles present in the air create health problems and affect the ecosystems of the planet. Scientists and environmental organizations have been looking for new ways to combat and control the air pollution, developing new solutions as technologies evolves. In the last decade, devices able to observe and maintain pollution levels have become more accessible and less expensive, and with the appearance of the Internet of Things (IoT), new approaches for combating pollution were born. The focus of the research presented in this paper was predicting behaviours regarding the air quality index using machine learning. Data were collected from one of the six atmospheric stations set in relevant areas of Bucharest, Romania, to validate our model. Several algorithms were proposed to study the evolution of temperature depending on the level of pollution and on seve...

Sensors

With the advent of the Smart Agriculture, the joint utilization of Internet of Things (IoT) and Machine Learning (ML) holds the promise to significantly improve agricultural production and sustainability. In this paper, the design of a Neural Network (NN)-based prediction model of a greenhouse’s internal air temperature, to be deployed and run on an edge device with constrained capabilities, is investigated. The model relies on a time series-oriented approach, taking as input variables the past and present values of the air temperature to forecast the future ones. In detail, we evaluate three different NN architecture types—namely, Long Short-Term Memory (LSTM) networks, Recurrent NNs (RNNs) and Artificial NNs (ANNs)—with various values of the sliding window associated with input data. Experimental results show that the three best-performing models have a Root Mean Squared Error (RMSE) value in the range 0.289÷0.402∘C, a Mean Absolute Percentage Error (MAPE) in the range of 0.87÷1.0...

Journal of Artificial Intelligence, Machine Learning and Data Science, 2025

Workday Extend provides a robust platform for building custom finance and people applications, leveraging a unified data source and security model. By utilizing thousands of publicly available REST and SOAP APIs, developers can quickly create solutions that integrate seamlessly with existing Workday applications. The platform further supports the integration of Workday's latest AI innovations, enhancing the capabilities of applications and improving the user experience. Combining Workday Journeys and Workday Prism Analytics, Workday Extend optimizes both the user and data experience. Mobile-enabled apps ensure an "always-on" experience, while app templates, packaged solutions and approved service partners streamline development. The Workday Extend app catalog provides templates for various use cases, including charitable donations, tuition reimbursement, commuting options, project forecasting and employee recognition. This flexibility enables organizations to effectively scale their solutions. The increasing integration of Artificial Intelligence (AI) and Machine Learning (ML) into enterprise applications is transforming business operations. Workday Extend leverages AI and ML technologies to optimize business processes, improve decision-making and enable automation at scale. This paper explores the role of AI and ML in shaping the future of enterprise applications within the Workday Extend ecosystem. It discusses the potential benefits, challenges and use cases of these technologies in business workflows and presents a vision of how these innovations will influence the future of enterprise solutions.

The integration of Artificial Intelligence (AI) into climate change mitigation strategies represents a transformative approach to addressing one of the most pressing global challenges. This article explores the multifaceted contributions of AI technologies in enhancing climate action efforts. It highlights how AI-driven innovations are optimizing energy systems through advanced predictive modeling and real-time data analytics, leading to more efficient energy consumption and reduced greenhouse gas emissions. Furthermore, the article examines the role of AI in climate modeling and simulations, which provide more accurate projections of climate impacts and inform strategic decision-making. The deployment of AI in monitoring environmental changes, managing natural resources, and supporting sustainable practices in agriculture and urban planning is also discussed. By leveraging AI’s capabilities in pattern recognition, automation, and data integration, stakeholders can develop more effective mitigation strategies and adapt to the evolving climate landscape. This comprehensive review underscores the potential of AI as a pivotal tool in advancing global climate goals and fostering resilient environmental stewardship.

Engineering with Computers, 2011

High temperatures within a data center can cause a number of problems, such as increased cooling costs and increased hardware failure rates. To overcome this problem, researchers have shown that workload management, focused on a data center's thermal properties, effectively reduces temperatures within a data center. In this paper, we propose a method to predict a workload's thermal effect on a data center, which will be suitable for real-time scenarios. We use machine learning techniques, such as artificial neural networks (ANN) as our prediction methodology. We use real data taken from a data center's normal operation to conduct our experiments. To reduce the data's complexity, we introduce a thermal impact matrix to capture the spacial relationship between the data center's heat sources, such as the compute nodes. Our results show that machine learning techniques can predict the workload's thermal effects in a timely manner, thus making them well suited for real-time scenarios. Based on the temperature prediction techniques, we developed a thermal-aware workload scheduling algorithm for data centers, which aims to reduce power consumption and temperatures in a data center. A simulation study is carried out to evaluate the performance of the algorithm. Simulation results show that our algorithm can significantly reduce temperatures in data centers by introducing an endurable decline in performance.

2009

The growth of the city of Athens in the last decades and the phenomenon of urbanisation obviously have led to the creation of a microclimate with explicit effects on human thermal comfort-discomfort. The knowledge of population thermal comfort-discomfort levels, predictable for the next days, is very important for suitable actions in order to protect public health.

IEEE Access, 2017

Industrial buildings are demonstrating increasing rates of energy consumption, with heating, ventilation, and air conditioning (HVAC) typically constituting over 50% of this consumption. However, these energy requirements are heavily influenced by weather conditions based on the season, the time of day, and different in-building activities. These activities take place in industrial setup over 24 h and have different HVAC energy requirements. In this paper, we propose a binary (0,1) integer linear programming approach to efficiently schedule activities based on weather forecasting, thus minimizing the energy required by HVAC. Experimental results show that energy consumption can be reduced by up to 30%.