Dynamic Nonlinear Indoor Environment Thermal State Estimation With Unknown Inputs Using PSO Guided Regularizer Based Adaptive EKF

The present paper shows how the dynamic indoor temperature profile of an HVAC (Heating, Ventilation, and Air Conditioning) system in a building can be developed using Kalman filters, in presence of unknown inputs. An RC network based dynamic, nonlinear thermal model is first developed for the indoor environment with a novel consideration of relative humidity factor. Then an extended Kalman Filter based algorithm in presence of unknown inputs (called
EKF-UI
) and an adaptive variation of this
EKF-UI
algorithm (called
AdEKF-UI
) are developed for the real indoor environment under consideration. Next, a particle swarm optimization (
PSO
) guided adaptive extended Kalman filter with unknown inputs (
PSOgAdEKF-UI
) algorithm is proposed to overcome limitations of the
EKF-UI
and
AdEKF-UI
algorithms, especially under bad initialization situations. This
PSOgAdEKF-UI
algorithm proposes an effective utilization of regularizer based initializations for the initial state estimation error covariance matrix and the measurement noise covariance matrix. Extensive experiments showed that, overall,
PSOgAdEKF-UI
algorithm could outperform
EKF-UI
and
AdEKF-UI
algorithms by 46.59% and 20.66%, respectively, in terms of mean square error, while estimating an unknown state.
Note to Practitioners
—This paper was motivated to estimate the nonlinear dynamics of indoor HVAC thermal profile in presence of unknown inputs. The study explores a proposed Kalman filter-based heuristic regularizer-assisted adaptive filtering methodology for nonlinear state estimation that can circumvent the constraints imposed by current approaches. The proposed method demonstrates its applicability in actual nonlinear physical systems since many matrices needed for such state estimation algorithms do not have accurate initialization information. The nature of inferential stochastic inputs in practical HVAC system can be evaluated utilizing our novel state estimation method of the altering relative humidity coupled nonlinear dynamic thermal model. The thermal profile of a practical HVAC system, in presence of varying unknown inputs, can be more accurately modeled when temporal variations in relative humidity are included in the nonlinear dynamic model, as an additional influencing factor.