Data-driven methods for building control — A review and promising future directions

Maddalena, Emilio T.; Lian, Yingzhao; Jones, Colin N.

doi:10.1016/j.conengprac.2019.104211

Cited by 100 publications

(52 citation statements)

References 173 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent studies, machine learning methods are used to independently achieve the correct parameters. For example, intelligent controllers, optimized by the use of evolutionary algorithms were developed for the control of the subsystems of an intelligent building [11]. The combination of machine learning, fuzzy control, and evolutionary algorithms culminated in the so-called Computational Intelligence (CI), which is currently being used in architecture.…”

Section: Related Workmentioning

confidence: 99%

Indoor Air Quality Control Using Backpropagated Neural Networks

Uskenbayeva¹,

Altayeva²,

Gusmanova³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Providing comfortable indoor air quality control in residential construction is an exceedingly important issue. This is due to the structure of the fast response controller of air quality. The presented work shows the breakdown and creation of a mathematical model for an interactive, nonlinear system for the required comfortable air quality. Furthermore, the paper refers to designing traditional proportional integral derivative regulators and proportional, integral, derivative regulators with independent parameters based on a backpropagation neural network. In the end, we perform the experimental outputs of a suggested backpropagation neural network-based proportional, integral, derivative controller and analyze model results by applying the proposed system. The obtained results demonstrated that the proposed controller can provide the required level of clean air in the room. The proposed developed model takes into consideration international Heating, Refrigerating, and air conditioning standards as ASHRAE AND ISO. Based on the findings, we concluded that it is possible to implement a proposed system in homes and offer equivalent indoor air quality with continuous mechanical ventilation without a profuse amount of energy.

show abstract

Section: Related Workmentioning

confidence: 99%

Indoor Air Quality Control Using Backpropagated Neural Networks

Uskenbayeva¹,

Altayeva²,

Gusmanova³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…In this regard, several types of MPC algorithms have been applied to HVAC systems in the literature [13][14][15][16][17][18][19][20][21][22][23][24][25][26]; see also [27,28] and the references therein. In particular, [15] presents two stochastic MPC algorithms, i.e.…”

Section: Modeling and Control Strategies For Buildingsmentioning

confidence: 99%

Scenario-based Model Predictive Control Approach for Heating Systems in an Office Building

Pippia

Lago

Coninck

et al. 2019

2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)

View full text Add to dashboard Cite

In the context of building heating systems control in office buildings, the current state-of-the-art applies either a deterministic Model Predictive Control (MPC) controller together with a nonlinear model, or a linearized model with a stochastic MPC controller. Deterministic MPC considers only one realization of the external disturbances, which can lead to a low performance solution if the forecasts of the disturbances are not accurate. Similarly, linear models are simplified representations of the building dynamics and might fail to capture some relevant behavior. In this paper, we improve upon the current literature by combining these two approaches, i.e. we adopt a nonlinear model together with a stochastic MPC controller. We consider a scenario-based MPC (SBMPC), where many realizations of the disturbances are considered, so as to include more possible future trajectories for the external disturbances. The adopted scenario generation method provides statistically significant scenarios, whereas so far in the current literature only approximate methods have been applied. Moreover, we use Modelica to obtain the model description, which allows to have a more accurate and nonlinear model. Lastly, we perform simulations comparing standard MPC vs SBMPC vs an optimal control approach with measurements of the external disturbances, and we show how our proposed scenario-based MPC controller can achieve a better performance compared to standard deterministic MPC.

show abstract

“…Recent research approaches cover the fields of (robust) model predictive control (MPC) (see e.g., [6][7][8][9]), adaptive or learning-based MPC (see e.g., [10]), and reinforcement learning (RL); see e.g., [11][12][13]. A comprehensive overview of MPC and data-driven approaches for building control can be found in [14,15]. Yet many of the aforementioned studies suffer from the non-standardized evaluation of their control performance.…”

Section: Introductionmentioning

confidence: 99%

Energym: A Building Model Library for Controller Benchmarking

et al. 2021

View full text Add to dashboard Cite

We introduce the Python-based open-source library Energym, a building model library to test and benchmark building controllers. The incorporated building models are presented with a brief explanation of their function, location and technical equipment. Furthermore, the library structure is described, highlighting the necessary features to provide the benchmarking and control capabilities, i.e., standardized evaluation scenarios, key performance indicators (KPIs) and forecasts of uncertain variables. We go on to characterize the evaluation scenarios for each of the models and give formal definitions of the KPIs. We describe the calibration methodologies used for constructing the models and illustrate their usage through examples.

show abstract

Data-driven methods for building control — A review and promising future directions

Cited by 100 publications

References 173 publications

Indoor Air Quality Control Using Backpropagated Neural Networks

Indoor Air Quality Control Using Backpropagated Neural Networks

Scenario-based Model Predictive Control Approach for Heating Systems in an Office Building

Energym: A Building Model Library for Controller Benchmarking

Contact Info

Product

Resources

About