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The aim of this study is to present a novel data-driven approach developed for space heating energy demand calculation of the whole EU building stock. To develop a computationally efficient bottom-up model that takes into account building physics parameters and details of the building stock make-up, an artificial neural network (ANN) is trained on a dataset of precise building-physics models. For this purpose, 2025 building energy simulations were performed in this study, ensuring...
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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...
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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...
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The climate system is an excellent example of a “complex system” since there is an interplay and inter-relation of several climate variables. Variables such as the Standardized Precipitation Index (SPI) are used to indicate the drought climate condition – a negative (or positive) value of SPI would imply a dry (or wet) state in a region. It is difficult to identify the factors influencing the SPI or their inter-relations (including feedback loops). Here, we aim to study the complex dynamics...
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The climate system is an excellent example of a “complex system” since there is an interplay and inter-relation of several climate variables. Variables such as the Standardized Precipitation Index (SPI) are used to indicate the drought climate condition – a negative (or positive) value of SPI would imply a dry (or wet) state in a region. It is difficult to identify the factors influencing the SPI or their inter-relations (including feedback loops). Here, we aim to study the complex dynamics...
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Cauvery Delta Zone is present in the eastern part of Tamil Nadu, popularly known as ‘rice bowl’ of Tamil Nadu. It constitutes of about 11.1% of total area of the state. It lies between 10.00 and 11.30° N latitude and 78.15°–79.45° E longitude. Cauvery Delta Zone includes the districts of Thanjavur, Thiruvarur, Nagapattinam, Mayiladuthurai, Perambalur and some parts of Pudhukottai and Cuddalore Districts. This zone plays a vital role not only in rice production but also in other varieties of...
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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...