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This paper discusses the challenges to net zero energy houses to the next level, which is to take them off the grid. This raises the stakes significantly as there is no reliance on the infinite storage that the grid provides, but only on the local energy storage. Overall reliability and resilience are the basic demands that need to be satisfied by the design and system sizing. We investigate a special aspect of power reliability, arguing that maintaining thermal comfort and guaranteeing power availability will be the two most important performance indicators in an off-grid house design. Due to the dynamic characteristic of a renewable energy generation system it is often difficult to maintain a stable thermal comfort level without investing in an oversized renewable power system. This paper presents a stochastic model based energy management system that distributes energy based on acceptable ranges of thermal comfort levels and manages electricity storage in batteries, driven by the projected energy demand and the projected energy generation. The objective of the stochastic model based control is to guide system operation and reach a balance between acceptable thermal comfort and general power reliability in an off-grid residence. Simulation results indicate that the proposed stochastic model based controller decreases the risk of power unavailability by up to 24% without sacrificing indoor thermal comfort dramatically. A follow-up sensitivity analysis demonstrates versatilities of the controller in practical application. Certain limitations in its application are discussed at the end of this paper.

The subject of this paper is the design analysis process of an experimental solar house, driven by the overarching goal to obtain net-zero energy performance while being functionally optimal and architecturally ambitious. The house was commissioned to participate in an international solar home competition called the Solar Decathlon. The paper demonstrates the use of simulation to support design decisions at various stages of the design process. Through it, the paper highlights attributes of simulation tools that are needed for supporting the design process effectively. In addition, this paper shows a novel use of building simulation by extending it to also inform the final use and operation of the house during the competition period. Finally, the paper also contributes to the design of solar homes by showing how their performance assessment and evaluation criteria can be different from homes that are served by the grid.

The emergence of Information Technology (IT) based sensing has received increasing attention and acceptance in buildings due to its noninvasive nature and its ability in delivering real-time and potentially highly personalized feedback to building energy and comfort management. This study presents results of a pilot deployment experiment on such an IT-based sensing system - Personal Office Energy Monitor (POEM) developed by Intel Labs. The pilot study shows that with appropriate analytic methods the POEM sensor data could be transferred into valuable inputs to building management system (BMS). This study applies building science principle based models as the first step to calculate intermediate building performance indices based on raw measurement data. The intermediate performance indices are then further analyzed in order to reveal potential means to improve a building's operational energy efficiency and occupant comfort. Results demonstrate that POEM sensor data could lead to energy saving opportunities through localized comfort management, plug load sensing and scheduling, and occupancy based building control. As an IT platform-integrated and occupant centered sensing system, POEM provides a convenient, low-cost, and efficient sensing solution to the next generation of smart buildings, featured by its ability in assisting BMS to improve operational building energy efficiency without compromising occupants' thermal comfort and indoor environmental quality.

Buildings account for approximately 40% of the primary energy consumption in the United States. As of 2009, 72% of residential buildings were built prior to 1990 and 86% were built before 2000. As of mid-2011, commercial buildings in the United States had an average age of 50 years. It is foreseeable that these older buildings must undergo repair and upgrades over the next few decades to be brought to the energy efficiency level mandated by energy codes. The current market for building energy efficiency retrofits is estimated around $279 billion with a projected energy cost savings of $1 trillion over a ten year period for buildings built prior to 1980. Yet the current building energy efficiency market primarily focuses on new construction; less than 10% of the high performance building case studies in the public database are of retrofitted buildings. This research reports results from a recent review study. The review study targets the building retrofit market sector specifically, and explores the current market size and why the building retrofit market has not expanded as much in past years. Review of past practice and research efforts reveals that though a building retrofit project, especially a deep retrofit project, is no different than any investment case in business practice, past retrofit projects are rarely motivated and convinced by the cost-effectiveness of the project but more by building owners' desire to “green” the building. Proving the cost-effectiveness and providing informed risk of a building retrofit project are critical in order for the market to move forward and reach a more matured state. Achieving a scaled and healthy retrofit market not only requires technical advance in delivering a building upgrade design but also demands more feedback information from existing building retrofit case studies regarding retrofit performance measurement and verification. The latter is particularly important in improving project confidence among building owners and 3rd party investors.

Calibrated detailed energy models are often used to identify economic deep retrofit opportunities in existing buildings. But the uncertainty in building performance makes it technically unrealistic to reach a single “best fit” model without extensive sub-metering and domain experts on the project which is typically labor intensive. To address this problem, researchers have been adopting stochastic modeling as a more reliable approach to calibrate building energy models. A set of all plausible models is found, rather than a best fit model, which accounts for uncertainties in existing building properties and conditions. In addition, sensor data collected from within a building can be used to identify key operational characteristics such as setpoint temperatures, carbon-dioxide levels, light levels, and temperature setbacks. This paper presents a hybrid calibration methodology for building energy models using a combination of short-term wireless sensor data, 15-min interval smart meter data and stochastic modeling. The hybrid approach provides a means to calibrate the operational variables and physical variables separately, reducing potential bias and errors and to reach a set of plausible model solutions. A case study is presented to demonstrate the strength of the calibration methodology.