ReViCEE: A recommendation based approach for personalized control, visual comfort & energy efficiency in buildings

Human Comfort in Indoor Environment: A Review on Assessment Criteria, Data Collection and Data Analysis Methods

“…To address the needs for achieving both personalized visual comfort and energy efficiency in open-plan office environment, the research work presented in [29] proposed a novel intelligent algorithm termed as ReViCEE to provide recommendations for optimum control of building lighting system. In the study, the researchers relied on distributed wireless sensor actuator network (WSAN) to collect the data.…”

“…On the other hand, the integration of machine learning algorithms with automatic control systems in commercial and residential buildings proves the concept of ''smart buildings'' with the aim to save energy, ensure security or improve occupants' comfort [29]. As an application example in indoor human behaviour study, data-driven models have been developed for the estimation of building occupancy which can assist in emergency response flow and supporting decision making mechanism [30], [31].…”

“…As an application example in indoor human behaviour study, data-driven models have been developed for the estimation of building occupancy which can assist in emergency response flow and supporting decision making mechanism [30], [31]. Another example is a recommendation system proposed in [29] to demonstrate the use of machine learning techniques for intelligent building lighting controls that are capable of meeting the needs of both individual visual comfort and energy efficiency of the building.…”

The emergence of explainability of intelligent systems: Delivering explainable and personalized recommendations for energy efficiency

“…The collaborative filtering algorithm in the second phase, performs a kNN clustering of users, which is the basis for sending the same tailored recommendations to all users of the same cluster. The ReViCEE recommendation system 14 provides personalized recommendations to reduce wasted energy in a university campus building in Singapore. The system learns end‐user preferences via the analysis of historical power consumption footprints.…”

Analysis of the impact of automatic shading control scenarios on occupant’s comfort and energy load

“…According to the International Energy Agency, the building sector energy demand is expected to rise up by 20% till 2040 [1] in which it is expected to consume more by 2.1% in non-industrial countries. This trend demands thinking of using effective measurements to minimize building energy load [2,3] while maximizing indoor environmental quality. The former is related to the cost, while the latter is associated with user comfort.…”