INCHEM-Py: An open source Python box model for indoor air chemistry

Abstract. AtChem is an open source zero-dimensional box-model for atmospheric chemistry. Any general set of chemical reactions can be used with AtChem, but the model was designed specifically for use with the Master Chemical Mechanism (MCM, http://mcm.york.ac.uk/). AtChem was initially developed within the EUROCHAMP project as a web application (AtChem-online, https://atchem.leeds.ac.uk/webapp/) for modelling environmental chamber experiments; it was recently upgraded and further developed into a standalone offline version (AtChem2) which allows the user to run complex and long simulations, such as those needed for modelling of intensive field campaigns, as well as to perform batch model runs for sensitivity studies. AtChem is installed, set up and configured using semi-automated scripts and simple text configuration files, making it easy to use even for non-experienced users. A key feature of AtChem is that it can easily be constrained to observational data which may have different timescales, thus retaining all the information contained in the observations. Implementation of a continuous integration workflow, coupled with a comprehensive suite of tests and version control software, makes the AtChem codebase robust, reliable and traceable. The AtChem2 code and documentation are available at https://github.com/AtChem/, under the open source MIT license.

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Modeling indoor air pollution from cookstove emissions in developing countries using a Monte Carlo single-box model

Atmospheric Environment ◽

10.1016/j.atmosenv.2011.03.044 ◽

2011 ◽

Vol 45 (19) ◽

pp. 3237-3243 ◽

Cited By ~ 57

Author(s):

Michael Johnson ◽

Nick Lam ◽

Simone Brant ◽

Christen Gray ◽

David Pennise

Keyword(s):

Air Pollution ◽

Developing Countries ◽

Monte Carlo ◽

Indoor Air ◽

Indoor Air Pollution ◽

Box Model

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An Online Grey-Box Model Based on Unscented Kalman Filter to Predict Temperature Profiles in Smart Buildings

Energies ◽

10.3390/en13082097 ◽

2020 ◽

Vol 13 (8) ◽

pp. 2097 ◽

Cited By ~ 1

Author(s):

Marco Massano ◽

Edoardo Patti ◽

Enrico Macii ◽

Andrea Acquaviva ◽

Lorenzo Bottaccioli

Keyword(s):

Kalman Filter ◽

Internet Of Things ◽

Air Temperature ◽

Indoor Air ◽

Unscented Kalman Filter ◽

Box Model ◽

Indoor Temperature ◽

Thermal Dynamics ◽

Thermal Network ◽

Realistic Data

Nearly 40% of primary energy consumption is related to the usage of energy in Buildings. Energy-related data such as indoor air temperature and power consumption of heating/cooling systems can be now collected due to the widespread diffusion of Internet-of-Things devices. Such energy data can be used (i) to train data-driven models than learn the thermal properties of buildings and (ii) to predict indoor temperature evolution. In this paper, we present a Grey-box model to estimate thermal dynamics in buildings based on Unscented Kalman Filter and thermal network representation. The proposed methodology has been applied in two different buildings with two different thermal network discretizations to test its accuracy in indoor air temperature prediction. Due to a lack of a real-world data sampled by Internet of Things (IoT) devices, a realistic data-set has been generated using the software Energy+, by referring to real industrial building models. Results on synthetic and realistic data show the accuracy of the proposed methodology in predicting indoor temperature trends up to the next 24 h with a maximum error lower than 2 °C, considering one year of data with different weather conditions.

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