Search

Model predictive control (MPC) is a recent development that uses modelling and simulation with forecast conditions to exploit building thermal mass in an effort to improve indoor thermal comfort and reduce energy use/cost compared with traditional rule-based control strategies. This paper investigates a range of recent MPC strategies and the direction in which this new technology is trending. The findings indicate that there is little evidence that directly compares the performance of specific optimization algorithms, forecast and simulation parameters (timestep, horizon), and climate forecast accuracy for the same scenario. An analysis of 19 case studies highlights the advantages of MPC compared with conventional control strategies, but also identifies areas that need further improvement. These areas include the optimization strategy, the effects of forecast disturbance assessment, and desirable traits of existing buildings under consideration for MPC implementation. This paper develops a set of target parameters for the types of buildings for which MPC will have the most impact and suggests methods for overcoming shortfalls identified in recently published studies. [ABSTRACT FROM AUTHOR]

Detailed domestic stock energy models can be used to help formulate optimum energy reduction strategies. However, there will always be considerable uncertainty related to their predictions due to the complexity of the housing stock and the many assumptions required to implement the models. This paper presents a simple Monte Carlo (MC) model that can be easily extended and/or transformed in relation to data available for investigating and quantifying uncertainties in both the housing stock model's predictions and scenario assumptions. While 90% of the MC model predictions fell within a range which is ±19% the mean value, 50% of them were within ±8% of the mean. The findings suggest that the uncertainties associated with the model predictions and scenario assumptions need to be acknowledged fully and – where possible – quantified as even fairly small variability in the influential variables may result in rather large uncertainty in the aggregated model's prediction. [ABSTRACT FROM PUBLISHER]

The paper provides a methodology for the optimal control of heating, ventilation, and air conditioning (HVAC) systems used in public buildings, with the purpose of obtaining high comfort and safety standards along with energy efficiency. The combination of the two concurrent objectives of minimizing energy use and guaranteeing high standards of occupant comfort is obtained by means of multi-objective optimization, in which a comfort model is combined along with a dynamic energy model of the building. The use of dynamic setpoints for the HVAC and the inclusion of comfort indicators represent a step forward, compared to the current design and operation procedures suggested by technical standards. The utilization of the proposed methodology is tested with reference to a case study, represented by an academic building used by the University of Pisa for educational purposes, whose extensive and variable occupancy can help to emphasize the importance of comfort in the operation of HVAC systems in different climatic conditions and with different occupancy profiles. We show how this optimization brings interesting results in terms of energy-saving (up to 30%), obtaining an increased comfort level (of more than 25%) compared to the operating conditions suggested by technical standards. [ABSTRACT FROM AUTHOR]

A model predictive control (MPC) system with an adaptive building model based on thermal-electrical analogy for the hybrid air conditioning system using the radiant floor and all-air system for heating is proposed in this paper to solve the heating supply control difficulties of the railway station on Tibetan Plateau. The MPC controller applies an off-line method of updating the building model to improve the accuracy of predicting indoor conditions. The control performance of the adaptive MPC is compared with the proportional-integral-derivative (PID) control, as well as an MPC without adaptive model through simulation constructed based on a TRNSYS-MATLAB co-simulation testbed. The results show that the implementation of the adaptive MPC can improve indoor thermal comfort and reduce 22.2% energy consumption compared to the PID control. Compared to the MPC without adaptive model, the adaptive MPC achieves fewer violations of constraints and reduces energy consumption by 11.5% through periodic model updating. This study focuses on the design of a control system to maintain indoor thermal comfort and improve system efficiency. The proposed method could also be applied in other public buildings. [ABSTRACT FROM AUTHOR]