Abstract — Heating, ventilation, and air-conditioning (HVAC) systems use a large amount of energy, and so they are an interesting area for efficiency improvements. The focus here is on the use of semiparametric regression to identify models, which are amenable to analysis and control system design, of HVAC systems. This paper briefly describes two testbeds that we have built on the Berkeley campus for modeling and efficient control of HVAC systems, and we use these testbeds as case studies for system identification. The main contribution of this work is that the use of semiparametric regression allows for the estimation of the heating load from occupancy, equipment, and solar heating using only temperature measurements. These estimates are important for building accurate models as well as designing efficient control schemes, and in our other work we have been able to achieve a reduction in energy consumption on a single room testbed using heating load estimation in conjunction with the learning-based model predictive control (LBMPC) technique. Furthermore, this framework is not restrictive to modeling nonlinear HVAC behavior, because we have been able to use this methodology to create hybrid system models that incorporate such nonlinearities. I.

Abstract—Cyber-Physical Systems (CPS) are characterized as complex distributed systems exhibiting substantial uncertainty due to interactions with the physical world. Today’s electric grids are often described as CPS because a portfolio of distributed supplies must be dispatched in real-time to match uncontrolled, uncertain demand while adhering to constraints imposed by the intervening transmission and distribution network. With the increased control complexity required by deep penetration of fluctuating renewable supplies, the grid becomes more profoundly a CPS and needs to be addressed as a system. In this evolving CPS, a large fraction of supply is under-actuated, a substantial portion of demand needs to become dispatchable, interactions among distributed elements are no longer unidirectional, and operating requirements of elements are more dynamic. To more sharply define these CPS challenges, we obtain a yearlong, detailed measurement of the real-time blend of supplies on the primary California grid dispatched to meet current demand and then scale the solar and wind assets, preserving uncontrolled weather effects, to a level of penetration associated with California’s 2050 GHG targets. In this representation of a future sustainable grid, we assess the impact of demand shaping, storage, and agility on the reconstituted supply portfolio, characterize resulting duration curves and ramping, and investigate the distributed control and management regime. We articulate new operational and market opportunities and challenges that may materialize from intermittent periods of abundance and scarcity in the overall energy network. We find that in a sustainable grid, lulls in renewable production during winter are more critical than peaks in demand during summer, capacity for load shifting and energy storage are more valuable as renewables penetration increases, and that grid balancing requires integrated management of supply and demand resources. Keywords-electricity; cyber-physical systems; smart grid; renewable energy I.

Abstract. HVAC systems account for 38 % of building energy usage. Studies have indicated at least 5-15 % waste due to unoccupied spaces being conditioned. Our goal is to minimize this waste by retrofitting HVAC systems to enable room-level zoning where each room is conditioned individually based on its occupancy. This will allow only occupied rooms to be conditioned while saving the energy used to condition unoccupied rooms. In order to achieve this goal, the effect of opening or closing air vent registers on room temperatures has to be predicted. Making such a prediction is complicated by the fact that weather has a larger effect on room temperatures than the settings of air vent registers, making it hard to isolate the influence of the HVAC system. We present a technique for dynamically estimating the heat load due to weather on room temperatures and subtracting it out in order to predict the effect of the HVAC system more directly.