scholarly journals Artificial Intelligence for Advanced Building Control: Energy and GHG Savings from a Case Study

Proceedings ◽  
2019 ◽  
Vol 23 (1) ◽  
pp. 7 ◽  
Author(s):  
Nunzio Cotrufo ◽  
Etienne Saloux
Keyword(s):  
Artificial Intelligence ◽  
Natural Gas ◽  
Predictive Control ◽  
Control Strategies ◽  
Weather Forecasts ◽  
Early Results ◽  
Advanced Control ◽  
Gas Consumption ◽  
Building Operation

Model-based Predictive Control (MPC) is a promising advanced control strategy for the improvement of building operation. MPC uses a model of the building along with weather forecasts to optimize control strategies, such as indoor air temperature set-points, thermal storage charging and discharging cycles, etc. An obstacle to the adoption of MPC is the modelling step: developing a dedicated control-oriented model is a time-consuming process, requiring technical expertise and a large amount of information about the building and its operation. To overcome these issues, this paper proposes a new approach for the development of MPC strategies based on Artificial Intelligence (AI) techniques, aiming to map correlations among commonly available operation variables and to develop models suitable for predictive control. The proposed approach was applied in an institutional building in Varennes, QC, with the aim of reducing the natural gas consumption during the heating season. Early results show a remarkable effectiveness of the proposed approach, with a reduction of natural gas and building heating consumption of 23.9% and 6.3%, respectively.

scholarly journals Efficient integration of IoT-based micro storages to improve urban drainage performance through advanced control strategies

2021 ◽  
Author(s):  
Martin Oberascher ◽  
Wolfgang Rauch ◽  
Robert Sitzenfrei
Keyword(s):  
Large Scale ◽  
Control Strategies ◽  
First Flush ◽  
Weather Forecasts ◽  
Advanced Control ◽  
Combined Sewer ◽  
Discharge Valve ◽  
Efficient Integration ◽  
Precipitation Events

Abstract The smart rain barrel (SRB) consists of a conventional rain barrel with storage volumes between 200 and 500 L, which is extended by a remotely (and centrally) controllable discharge valve. The SRB is capable to release stormwater prior precipitation events by using high-resolution weather forecasts to increase detention capacity. However, as shown in the previous work, a large-scale implementation combined with a simultaneously opening of discharge valves clearly reduce effectiveness. The aim of this work is to systematically investigate different control strategies for wet weather by evaluating their impact on sewer performance. For case study, an Alpine municipality is hypothetically retrofitted with SRBs (total additional storage volume of 181 m3). The results show that combined sewer overflow (CSO) volume and subsequently pollution mass can be reduced between 7 and 67% depending on rain characteristics (e.g., rain pattern, amount of precipitation) and applied control strategy. Effectiveness of the SRBs increases with lower CSO volume, whereas more advanced control strategies based on sewer conditions can clearly improve system's performance compared to simpler control strategies. For higher CSO volume, the SRBs can postpone start of an CSO event which is important for first-flush phenomenon.

PID, Fuzzy and Model Predictive Control Applied to a Practical Nonlinear Plant

2016 ◽  
Vol 4 (1) ◽  
pp. 19-42
Author(s):  
Kai Borgeest ◽  
Peter Josef Schneider
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Cooling System ◽  
Control Strategies ◽  
Fan Noise ◽  
Nonlinear Properties ◽  
Control Schemes ◽  
Advanced Control ◽  
Save Energy ◽  
Nonlinear Plant

For the cooling system of a mobile machine with m control variables and with n=m correction variables different control strategies have been investigated in order to minimize power to save energy and to reduce fan noise with sufficient cooling. The plant is nonlinear and not identified. Three different kinds of controllers have been investigated in several variations, i.e. fuzzy control, PI(D) and model predictive control (MPC). 14 different criteria have been used for evaluation. In many respects a linear controller with fuzzy prediction proved best, in particular the prediction model can handle nonlinear properties of the plant. A problem of advanced control schemes with unidentified plants is the difficulty to prove stability.

scholarly journals Prediction of Natural Gas Consumption for City-Level DHS Based on Attention GRU: A Case Study for a Northern Chinese City

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 130685-130699
Author(s):  
Guixiang Xue ◽  
Jiancai Song ◽  
Xiangfei Kong ◽  
Yu Pan ◽  
Chengying Qi ◽  
...  
Keyword(s):  
Natural Gas ◽  
Chinese City ◽  
Gas Consumption ◽  
Northern Chinese ◽  
Natural Gas Consumption

scholarly journals Advanced Control of a Continuous Solution Copolymerization Process

2011 ◽  
Vol 2011 ◽  
pp. 1-17 ◽  
Author(s):  
Nádson Murilo Nascimento Lima ◽  
Lamia Zuñiga Liñan ◽  
Flavio Manenti ◽  
Rubens Maciel Filho ◽  
Marcelo Embiruçu ◽  
...  
Keyword(s):  
Predictive Control ◽  
Continuous Stirred Tank Reactor ◽  
Industrial Case Study ◽  
Oil Refineries ◽  
Model Based ◽  
Control Robustness ◽  
Advanced Control ◽  
Predictive Controller ◽  
Continuous Stirred Tank

A model-based predictive control system is designed for a copolymerization reactor. These processes typically have such a high nonlinear dynamic behavior to make practically ineffective the conventional control techniques, still so widespread in process and polymer industries. A predictive controller is adopted in this work, given the success this family of controllers is having in many chemical processes and oil refineries, especially due to their possibility of including bounds on both manipulated and controlled variables. The solution copolymerization of methyl methacrylate with vinyl acetate in a continuous stirred tank reactor is considered as an industrial case study for the analysis of the predictive control robustness in the field of petrochemical and polymer production. Both regulatory and servo problems scenarios are considered to check tangible benefits deriving from model-based predictive controller implementation.

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