Model predictive control(MPC)is an advanced control technique.It has been deployed to harness the energy flexibility of a building.MPC requires a dynamic model of the building to achieve such an objective.However,deve...Model predictive control(MPC)is an advanced control technique.It has been deployed to harness the energy flexibility of a building.MPC requires a dynamic model of the building to achieve such an objective.However,developing a suitable predictive model is the main challenge in MPC implementation forflexibility activation.This studyfocuses on the application of key performance indicators(KPls)to evaluate the suitability of MPC models via feature selection.To this end,multiple models were developed for two houses.A feature selection method was developed to select an appropriate feature space to train the models.These predictive models were then quantified based on one-step ahead prediction error(OSPE),a standard KPI used in multiple studies,and a less-often KPl:multi-step ahead prediction error(MSPE).An MPC workflow was designed where different models can serve as the predictive model.Findings showed that MSPE better demonstrates the performance of predictive models used for flexibility activation.Results revealed that up to 57% of the flexibility potential and 48% of energy use reduction are not exploited if MSPE is not minimized while developing a predictive model.展开更多
基金funded by the Research Foundation Flanders(FWO),application number GOD2519Nby KU Leuven,grant C24/18/040.
文摘Model predictive control(MPC)is an advanced control technique.It has been deployed to harness the energy flexibility of a building.MPC requires a dynamic model of the building to achieve such an objective.However,developing a suitable predictive model is the main challenge in MPC implementation forflexibility activation.This studyfocuses on the application of key performance indicators(KPls)to evaluate the suitability of MPC models via feature selection.To this end,multiple models were developed for two houses.A feature selection method was developed to select an appropriate feature space to train the models.These predictive models were then quantified based on one-step ahead prediction error(OSPE),a standard KPI used in multiple studies,and a less-often KPl:multi-step ahead prediction error(MSPE).An MPC workflow was designed where different models can serve as the predictive model.Findings showed that MSPE better demonstrates the performance of predictive models used for flexibility activation.Results revealed that up to 57% of the flexibility potential and 48% of energy use reduction are not exploited if MSPE is not minimized while developing a predictive model.
