Integrating Smart Ceiling Fans and Communicating Thermostats to Provide Energy-Efficient Comfort

The project goal was to identify and test the integration of smart ceiling fans and communicating thermostats. These highly efficient ceiling fans use as much power as an LED light bulb and have onboard temperature and occupancy sensors for automatic operationbased on space conditions. The Center for the Environment (CBE) at UC Berkeley led the research team including TRC, Association for Energy Affordability (AEA), and Big Ass Fans (BAF). The research team conducted laboratory tests, installed99 ceiling fans and 12 thermostats in four affordable multifamily housing sites in California’s Central Valley, interviewed stakeholders to develop a case study, developed an online design tool and design guide, outlined codes and standards outreach, and published several papers.The project team raised indoor cooling temperature setpoints and used ceiling fans as the first stage of cooling; this sequencing of ceiling fans and air conditioningreducesenergy consumption, especially during peak periods, while providing thermal comfort.The field demonstration resulted in 39% measured compressor energy savings during the April–October cooling seasoncompared to baseline conditions, normalized for floor area. Weather-normalized energy use varied from a 36% increase to 71% savings, withmedian savings of 15%.This variability reflects the diversity in buildings, mechanical systems, prior operation settings, space types, andoccupants’ schedules,preferences, and motivations. All commercial spaces with regular occupancy schedules (and twoof the irregularly-occupied commercial spaces and one of the homes) showed energy savings on an absolute basis before normalizing for warmer intervention temperatures,and 10 of 13 sites showed energy savings on a weather-normalized basis. The ceiling fans provided cooling for one site for months during hot weather when the coolingequipment failed.Occupants reported high satisfaction with the ceiling fans and improved thermal comfort. This technology can apply to new and retrofit residential and commercial buildings

A Cognitive Social IoT Approach for Smart Energy Management in a Real Environment

Energy usage inside buildings is a critical problem, especially considering high loads such as Heating, Ventilation and Air Conditioning (HVAC) systems: around 50% of the buildings’ energy demand resides in HVAC usage which causes a significant waste of energy resources due to improper uses. Usage awareness and efficient management have the potential to reduce related costs. However, strict saving policies may contrast with users’ comfort. In this sense, this paper proposes a multi-user multi-room smart energy management approach where a trade-off between the energy cost and the users’ thermal comfort is achieved. The proposed user-centric approach takes advantage of the novel paradigm of the Social Internet of Things to leverage a social consciousness and allow automated interactions between objects. Accordingly, the system automatically obtains the thermal profiles of both rooms and users. All these profiles are continuously updated based on the system experience and are then analysed through an optimization model to drive the selection of the most appropriate working times for HVACs. Experimental results in a real environment demonstrated the cognitive behaviour of the system which can adapt to users’ needs and ensure an acceptable comfort level while at the same time reducing energy costs compared to traditional usage

Using Personal Environmental Comfort Systems to Mitigate the Impact of Occupancy Prediction Errors on HVAC Performance

Heating, Ventilation and Air Conditioning (HVAC) consumes a significant fraction of energy in commercial buildings. Hence, the use of optimization techniques to reduce HVAC energy consumption has been widely studied. Model predictive control (MPC) is one state of the art optimization technique for HVAC control which converts the control problem to a sequence of optimization problems, each over a finite time horizon. In a typical MPC, future system state is estimated from a model using predictions of model inputs, such as building occupancy and outside air temperature. Consequently, as prediction accuracy deteriorates, MPC performance--in terms of occupant comfort and building energy use--degrades. In this work, we use a custom-built building thermal simulator to systematically investigate the impact of occupancy prediction errors on occupant comfort and energy consumption. Our analysis shows that in our test building, as occupancy prediction error increases from 5\% to 20\% the performance of an MPC-based HVAC controller becomes worse than that of even a simple static schedule. However, when combined with a personal environmental control (PEC) system, HVAC controllers are considerably more robust to prediction errors. Thus, we quantify the effectiveness of PECs in mitigating the impact of forecast errors on MPC control for HVAC systems.Comment: 21 pages, 13 figure

Microservices and Machine Learning Algorithms for Adaptive Green Buildings

In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Occupant behaviour in naturally ventilated and hybrid buildings

Adaptive thermal comfort criteria for building occupants are now becoming established. In this paper we illustrate their use in the prediction of occupant behaviour and make a comparison with a non-adaptive temperature threshold approach. A thermal comfort driven adaptive behavioural model for window opening is described and its use within dynamic simulation illustrated for a number of building types. Further development of the adaptive behavioural model is suggested including use of windows, doors, ceiling fans, night cooling, air conditioning and heating, also the setting of opportunities and constraints appropriate to a particular situation. The integration in dynamic simulation of the thermal adaptive behaviours together with non-thermally driven behaviours such as occupancy, lights and blind use is proposed in order to create a more complete model of occupant behaviour. It is further proposed that this behavioural model is implemented in a methodology that includes other uncertainties (e.g. in internal gains) so that a realistic range of occupant behaviours is represented at the design stage to assist in the design of robust, comfortable and low energy buildings