A Markov-Switching model for building occupant activity estimation

Wolf, Sebastian; Møller, Jan Kloppenborg; Bitsch, Magnus Alexander; Krogstie, John; Madsen, Henrik

doi:10.1016/j.enbuild.2018.11.041

Cited by 33 publications

(15 citation statements)

References 25 publications

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“…Other researchers such as Xaba et al (2015) and Alvarez et al (2018) employ the regime switching models but the focus was not on predicting the regime shifts in the JSE-ALSI. For more reading about nonlinear and regime-shifts, see Hamilton, (2010); Chkili and Nguyen (2014); Ardia et al (2018); and Wolf et al (2019).…”

Section: Introductionmentioning

confidence: 99%

Predicting Regime Shifts in Johannesburg Stock Exchange Allshare Index (Jse-Alsi): A Markov-Switching Approach

Makatjane¹,

Molefe²

2020

EJEF

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It has been proven several times that linear models are unable to encapsulate nonlinear dynamics of macroeconomic and financial data such as inflation rates, exchange rates and stock prices to mention fewer. As a result, to overcome this problem, this current study adopted the nonlinear models due to the fact that they have required qualities to apprehend nonlinearity in a dataset. In order to predict a regime shifts, a five-day Johannesburg stock exchange allshare index (JSE-ALSI) spanning period from 02 January 2003 to 28 June 2019 was used as an experimental unit. This current study firstly employed Teräsvirta neural network test to detect the presence of nonlinearity and proceeded to estimate a two regime Markov-Switching autoregressive (MS-AR). The results of Teräsvirta neural network test revealed a highly significant nonlinearity with permanent seasonality as demonstrated by Kruskal-Wallis test. The predicted regime shifts by a latent dynamic allowed the autoregressive and variance parameters to promptly react to vital systemic shocks. As a result, this current study allowed volatility to oscillate between high and low volatility regimes that produced an expected duration of high volatility of two year and two months. This was a clear indication that there is a regime shifts in JSE-ALI which are modeled using Markov-Chain (MC) stochastic process. These findings may be used to inform robust policy making with the aim of safeguarding both the JSE and other global stock markets from the episodes of stock market crash. Moreover, other researchers can utilize the results of this study to calculate the risk associated with structural breaks and high volatility periods.

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Section: Introductionmentioning

confidence: 99%

Predicting Regime Shifts in Johannesburg Stock Exchange Allshare Index (Jse-Alsi): A Markov-Switching Approach

Makatjane¹,

Molefe²

2020

EJEF

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“…Figure 3 shows that values obtained in kitchens (K) are very different from those obtained in the master bedrooms (MB) or in children bedrooms (CB). Since CO 2 is a good indicator of occupancy, 57,58 the occupancy profile of each room can be detected. The collected data also show that the change of the MEV status influences CO 2 concentration values.…”

Section: Resultsmentioning

confidence: 99%

The impact of mechanical ventilation operation strategies on indoor CO₂ concentration and air exchange rates in residential buildings

Pereira

Ramos

2020

Indoor and Built Environment

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In Portugal, residential buildings commonly have their ventilation strategy changed after commissioning. This occurs due to the building managers' willingness to reduce shared costs with the electricity needed for fan operation. However, this option is not technically supported, and the effects of such a strategy on indoor air quality-related to human pollutants are yet to be quantified. CO2 was monitored in 15 bedrooms and air exchange rates were calculated for each room. The air exchange rate values ranged from 0.18 to 0.53 h−1 when mechanical extraction ventilation was off, and from 0.45 to 0.90 h−1 when mechanical extraction ventilation was on, which represents an average increase of 119%. With the current intermittent ventilation strategy, all rooms remain above 1500 ppm for a given percentage of time, and 12 rooms presenting CO2 concentrations above 2000 ppm. Simulations of theoretical CO2 concentrations, for a non-interrupted mechanical ventilation strategy show that no rooms would accumulate CO2 concentrations above 2000 ppm, and only 25% would present CO2 concentrations above 1500 ppm. Pearson correlations between the monitored CO2 and human and spatial factors identified two relevant parameters. Those parameters correspond to ratios between CO2 generation and floor area ([Formula: see text]), and airflow with CO2 generation ([Formula: see text]). The proposed ratios could be used as ways to optimise ventilation costs and indoor air quality.

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“…Apart from KNN models, classification models based on support vector machine (SVMs) [45,47], which are highly efficient for non-linear classification (using kernel), have also been frequently used. Models that use the Markov chain [48][49][50][51][52] or artificial neural networks (ANNs) [45,47,49,50,53] are also common. Similarly, different variants of regression models [20,29,[32][33][34]39,40,[54][55][56][57][58][59][60][61][62] and combinations of regression models that incorporate concepts such as the Theory of Planned Behaviour [37,38] and other classification models including reinforcement learning (RL) [63][64][65] have typically been used for modelling energy-related behaviour.…”

Section: Types Of Modelling Techniquesmentioning

confidence: 99%

A Meta-Analysis Review of Occupant Behaviour Models for Assessing Demand-Side Energy Consumption

2022

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Occupant behaviour plays a significant role in shaping the dynamics of energy consumption in buildings, but the complex nature of occupant behaviour has hindered a deeper understanding of its influence. A meta-analysis was conducted on 65 published studies that used data-driven quantitative assessments to assess energy-related occupant behaviour using the Knowledge Discovery and Data Mining (KDD) framework. Hierarchical clustering was utilised to categorise different modelling techniques based on the intended outcomes of the model and the types of parameters used in various models. This study will assist researchers in selecting the most appropriate parameters and methods under various data constraints and research questions. The research revealed two distinct model categories being used to study occupant behaviour-driven energy consumption, namely (i) occupancy status models and (ii) energy-related behaviour models. Multiple studies have identified limitations on data collection and privacy concerns as constraints of modelling occupant behaviour in residential buildings. The “regression model” and its variants were found to be the preferred model types for research that models “energy-related behaviour”, and “classification models” were found to be preferable for modelling “occupancy” status. There were only limited instances of data-driven studies that modelled occupant behaviour in low-income households, and there is a need to generate region-specific models to accurately model energy-related behaviour.

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A Markov-Switching model for building occupant activity estimation

Cited by 33 publications

References 25 publications

Predicting Regime Shifts in Johannesburg Stock Exchange Allshare Index (Jse-Alsi): A Markov-Switching Approach

Predicting Regime Shifts in Johannesburg Stock Exchange Allshare Index (Jse-Alsi): A Markov-Switching Approach

The impact of mechanical ventilation operation strategies on indoor CO₂ concentration and air exchange rates in residential buildings

A Meta-Analysis Review of Occupant Behaviour Models for Assessing Demand-Side Energy Consumption

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A Markov-Switching model for building occupant activity estimation

Cited by 33 publications

References 25 publications

Predicting Regime Shifts in Johannesburg Stock Exchange Allshare Index (Jse-Alsi): A Markov-Switching Approach

Predicting Regime Shifts in Johannesburg Stock Exchange Allshare Index (Jse-Alsi): A Markov-Switching Approach

The impact of mechanical ventilation operation strategies on indoor CO2 concentration and air exchange rates in residential buildings

A Meta-Analysis Review of Occupant Behaviour Models for Assessing Demand-Side Energy Consumption

Contact Info

Product

Resources

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The impact of mechanical ventilation operation strategies on indoor CO₂ concentration and air exchange rates in residential buildings