A two-step identification-optimization approach for climate change control

Sangiorgi, Lucia; Carnevale, Claudio

doi:10.1109/codit55151.2022.9803954

Cited by 2 publications

(1 citation statement)

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“…In fact, unlike these solutions, this approach is based on the identification of a data-driven model able to reproduce both short term (few days) and long term (up to a year) pollutant dynamics and follows a model predictive control approach (MPC). MPC is widely used on control system community in particular for industrial and robotic application [27], [28], while only limited study has been performed for air quality management and/or climate change control [29] [30]. Moreover, in order to take into account both short and long term dynamic, a hierarchical model predictive control problem has been formulated [31], where the control law computed for the short term dynamic is used as a constraints for the long term control problem.…”

Section: Introductionmentioning

confidence: 99%

A Model Predictive Control Methodology to Integrate Short and Long Term Air Quality Objectives

Sangiorgi,

Carnevale

2024

IEEE Access

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This study introduces and evaluates a methodology to define optimal integrated short and long-term air pollution control measures, to support policy formulation by Local Authorities. The approach utilized in this methodology is based on a receding horizon strategy. In this approach, an auto-regressive model provides understanding on the dynamic characteristics of air quality within a designated time period. The model is established using daily observed data on pollutant concentration, meteorological variables, and estimated emission data in the study area. At each time step, the resulting optimization problem is addressed using genetic algorithms. The effectiveness of the overall control has been assessed in the context of controlling NO 2 concentrations within the city of Milan. The outcomes of the study demonstrate that this control system can serve as a valuable tool to assist Local Authorities in making informed decisions regarding appropriate air quality management strategies.

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