Your search
Results 14 resources
-
This paper presents a methodology for the development and implementation of Model Predictive Control (MPC) in institutional buildings. This methodology relies on Artificial Intelligence (AI) for model development. An appropriate control-oriented model is a critical component in MPC; model development is no easy task, and it often requires significant technical expertise, effort and time, along with a substantial amount of information. AI techniques enable rapid development and calibration of...
-
To apply real-time predictive control using automated devices for minimizing the risk of surface condensation in a residential space, the authors first developed a nodal network model that simulates the flow of moist air and the thermal behavior of a target area with the given boundary conditions of a space. The lumped model was enhanced using a parameter estimation technique based on the measured temperature, humidity, and schedule data. However, the humidity model prediction performance...
-
Machine learning presents opportunities for tracking evidence on climate change adaptation, including text-based methods from natural language processing. In theory, such tools can analyse more data in less time, using fewer resources and with less risk of bias. However, the first generation of adaptation studies have delivered only proof of concepts. Reviewing these first studies, we argue that future efforts should focus on creating more diverse datasets, investigating concrete hypotheses,...
-
Model Predictive Control has gained much attention due to its potential to improve building operations by reducing costs, integrating renewable energy sources, and increasing thermal comfort. This paper aims to compare the accuracy of grey-box models based on resistance–capacitance (RC) networks and Long-Short-Term Memory (LSTM) neural networks in the prediction of the buildings’ thermal response, which is a key feature for the successful implementation of predictive controllers. Indoor air...
-
The objective of this research is to investigate air flow distribution inside a light weight test room which is naturally ventilated using artificial neural networks. The test room is situated in a relatively sheltered location and is ventilated through adjustable louvres. Indoor air temperature and velocity are measured at four locations and six different levels. The outside local temperature, relative humidity, wind velocity and direction are also monitored. The collected data are used to...
-
Personal thermal comfort models are crucial for the future of human-in-the-loop HVAC control in energy-efficient buildings. Individual comfort models, compared to average population responses, can provide the personalization required for successful control. In this work, we frame the thermal preference prediction task as a multivariate, multi-class classification problem and use deep learning and time-series-based approach for thermal preference prediction. We combine l1 regularization with...
-
Personal thermal comfort models are crucial for the future of human-in-the-loop HVAC control in energy-efficient buildings. Individual comfort models, compared to average population responses, can provide the personalization required for successful control. In this work, we frame the thermal preference prediction task as a multivariate, multi-class classification problem and use deep learning and time-series-based approach for thermal preference prediction. We combine l1 regularization with...
-
<b>Background</b> The global literature on the links between climate change and human health is large, increasing exponentially, and it is no longer feasible to collate and synthesise using traditional systematic evidence mapping approaches. We aimed to use machine learning methods to systematically synthesise an evidence base on climate change and human health. <br><b>Methods</b> We used supervised machine learning and other natural language processing methods (topic modelling and...
-
Climate change is already affecting health in populations around the world, threatening to undermine the past 50 years of global gains in public health. Health is not only affected by climate change via many causal pathways, but also by the emissions that drive climate change and their co-pollutants. Yet there has been relatively limited synthesis of key insights and trends at a global scale across fragmented disciplines. Compounding this, an exponentially increasing literature means that...
Explore
Theme
Publication year
-
Between 2000 and 2025
(14)
-
Between 2000 and 2009
(1)
- 2001 (1)
- Between 2010 and 2019 (2)
- Between 2020 and 2025 (11)
-
Between 2000 and 2009
(1)