Development and application of smart algorithms for control and management in buildings, towards zero energy buildings

Abstract

The scope of the present doctoral thesis is to develop and integrate building optimization and control algorithms which: (1) safeguard the comfort of occupants, (2) reduce the energy consumption of the HVAC equipment, (3) embody and manage the energy production from RES, (4) can be integrated in existing and new BEMS and (5) facilitate the transformation of any building towards a zero energy building. The main characteristics of the developed BOC algorithms are to: • Integrate predictive models for outdoor and indoor conditions to facilitate calculation of the performance of the HVAC systems. • Incorporate optimization algorithms which predict the optimum operation of the HVAC systems in the near future. • Apply close loop control which minimizes the difference between the set-points and the actual values. • Combine the calculations from the optimization with human interference, when required, in order to guarantee that potential failure of a subsystem is not affecting the whole BOC structure. The different sub-systems which compose the BOC algorithms are:  A closed loop control algorithm for safeguarding the comfort conditions and reduce the energy consumption from waste energy.  A predictive algorithm for outdoor conditions which affect the buildings’ fabric and the operation of the HVAC systems  A predictive algorithm for indoor conditions which estimate the indoor conditions under the predicted outdoor conditions and the use of the HVAC system  An optimization algorithm which sets the set-point of the AHU for the near future in order to exploit the thermal mass of the buildings’ fabric.  An override sequence which bypasses all the system and allows authorized/trained personnel to send commands directly to the HVAC and artificial lights. Thermal and lighting models of hospital facilities are developed and validated with collected measurements. The thermal models are used to preliminary estimate energy requirements of buildings and the comfort of occupants. Furthermore, the thermal modes are used for the fine-tuning of the control algorithms and the estimation of their energy efficiency potential. The thermal models incorporate the geometry of the buildings, the construction characteristics and the internal gains. The thermal models are connected with the BOC algorithms development software. The thermal models point the direction the developed algorithms should follow to transform the energy consuming buildings to zero energy ones. Advanced control algorithms for AHU and artificial lights are designed and fine-tuned to safeguard the comfort level and reduce the energy losses from wasted energy. The control algorithms use the knowledge from the users in the form of rules and their parameters are easily fine-tuned, if required. The reduction of the energy losses contribute to the target of zero energy buildings. Innovative identification algorithms are developed in order to estimate in advance the conditions of the facilities in order to adjust the usage of the HVAC equipment. The identification algorithms predict indoor and outdoor conditions. The a priori knowledge assists the definition of plans which can reduce the energy consumption of the next hours of operation, reducing the power demand for specific hours of the day. Furthermore, optimization algorithms using genetic techniques are used to select the most “profitable” operation of the HVAC system in the next hours. The solution selected from the optimization algorithm minimizes the operational cost of the HVAC system over the next hours while the comfort level can be maintained. The optimization algorithms can integrate additional energy efficiency technologies such as RES and swift the loads when RES provide power. The BOC algorithms are integrated in specific facilities of the two Hospitals (Hospital of Chania and Hospital of Ancona, Italy) and the energy efficiency are calculated at 57% and 55% for the Air handling Units and the artificial lights respectively for the hospital of Chania and 75 % for the artificial lights in the hospital of Ancona. The present thesis provides a completed innovative optimization and control system which can be applied to existing BEMS or new ones in order to maximize the energy efficiency of the systems. The optimization and control system has achieved significant energy efficiency in both pilot hospitals, without compromising the comfort (visual or thermal) of patients. Another significant advantage the new control algorithms is the ability to be accessed and monitored from distance by means of a Web-EMCS internet platform.