A self-organization formation configuration based assignment probability and collision detection

The formation control of multiple unmanned aerial vehicles(multi-UAVs)has always been a research hotspot.Based on the straight line trajectory,a multi-UAVs target point assignment algorithm based on the assignment probability is proposed to achieve the shortest overall formation path of multi-UAVs with low complexity and reduce the energy consumption.In order to avoid the collision between UAVs in the formation process,the concept of safety ball is introduced,and the collision detection based on continuous motion of two time slots and the lane occupation detection after motion is proposed to avoid collision between UAVs.Based on the idea of game theory,a method of UAV motion form setting based on the maximization of interests is proposed,including the maximization of self-interest and the maximization of formation interest is proposed,so that multi-UAVs can complete the formation task quickly and reasonably with the linear trajectory assigned in advance.Finally,through simulation verification,the multi-UAVs target assignment algorithm based on the assignment probability proposed in this paper can effectively reduce the total path length,and the UAV motion selection method based on the maximization interests can effectively complete the task formation.

Deep Weighted Fusion Learning(DWFL)-based multi-sensor fusion model for accurate building occupancy detection

With the advancement of artificial intelligence,the dominance of deep learning(DL)models over ordinary machine learning(ML)algorithms has become a reality in recent years due to its capability of handling complex pattern recognition without manual feature pre-definition.With the growing demands for power savings,building energy loss reduction could benefit from DL techniques.For buildings/rooms with the varying number of occupants,heating,ventilation,and air conditioning(HVAC)systems are often found in operations without much necessity.To reduce the building’s energy loss,accurate occupancy detection/prediction(ODP)results could be used to control the proper operations of HVACs.However,ODP is a challenging issue due to multiple reasons,such as improper selection/deployment of sensors,inefficient learning algorithms for pattern recognition,varying room conditions,etc.To overcome the above challenges,we propose a DL-based framework,i.e.,Deep Weighted Fusion Learning(DWFL),to detect and predict occupancy counts with optimal multi-sensor fusion structure.DWFL fuses the extracted features from multiple types of sensors with the priority/weight assignment to each sensor.Such weight assignment considers different room conditions and the pros/cons of each type of sensor.To evaluate DWFL model in terms of occupancy prediction accuracy,we have set up an experimental testbed with low-cost cameras,carbon dioxide(CO_(2)),and passive infrared(PIR)sensors.Among the recently proposed occupancy detection models,DeepFusion utilized deep learning model on heterogeneous sensor data and achieved 88%accuracy in occupancy count estimation(Xue et al.,2019).Another deep learning-based model MI-PIR achieved 91%accuracy on raw analog data from PIR sensors(Andrews et al.,2020).Our research outcome is 94%.Therefore,the experiment results show that our DWFL scheme outperforms the state-of-the-art ODP methods by 3%.

Impacts of technology-guided occupant behavior on air-conditioning system control and building energy use

Occupant behavior is an important factor affecting building energy consumption.Many studies have been conducted recently to model occupant behavior and analyze its impact on building energy use.However,to achieve a reduction of energy consumption in buildings,the coordination between occupant behavior and energy-efficient technologies are essential to be considered simultaneously rather than separately considering the development of technologies and the analysis of occupant behavior.It is important to utilize energy-efficient technologies to guide the occupants to avoid unnecessary energy uses.This study,therefore,proposes a new concept,“technology-guided occupant behavior”to coordinate occupant behavior with energy-efficient technologies for building energy controls.The occupants are involved into the control loop of central air-conditioning systems by actively responding to their cooling needs.On-site tests are conducted in a Hong Kong campus building to analyze the performance of“technology-guided occupant behavior”on building energy use.According to the measured data,the occupant behavior guided by the technology could achieve“cooling on demand”principle and hence reduce the energy consumption of central air-conditioning system in the test building about 23.5%,which accounts for about 7.8%of total building electricity use.

Machine Learning and Deep Learning Methods for Enhancing Building Energy Efficiency and Indoor Environmental Quality – A Review

The built environment sector is responsible for almost one-third of the world’s final energy consumption. Hence, seeking plausible solutions to minimise building energy demands and mitigate adverse environmental impacts is necessary. Artificial intelligence (AI) techniques such as machine and deep learning have been increasingly and successfully applied to develop solutions for the built environment. This review provided a critical summary of the existing literature on the machine and deep learning methods for the built environment over the past decade, with special reference to holistic approaches. Different AI-based techniques employed to resolve interconnected problems related to heating, ventilation and air conditioning (HVAC) systems and enhance building performances were reviewed, including energy forecasting and management, indoor air quality and occupancy comfort/satisfaction prediction, occupancy detection and recognition, and fault detection and diagnosis. The present study explored existing AI-based techniques focusing on the framework, methodology, and performance. The literature highlighted that selecting the most suitable machine learning and deep learning model for solving a problem could be challenging. The recent explosive growth experienced by the research area has led to hundreds of machine learning algorithms being applied to building performance-related studies. The literature showed that existing research studies considered a wide range of scope/scales (from an HVAC component to urban areas) and time scales (minute to year). This makes it difficult to find an optimal algorithm for a specific task or case. The studies also employed a wide range of evaluation metrics, adding to the challenge. Further developments and more specific guidelines are required for the built environment field to encourage best practices in evaluating and selecting models. The literature also showed that while machine and deep learning had been successfully applied in building energy efficiency research, most of the studies are still at the experimental or testing stage, and there are limited studies which implemented machine and deep learning strategies in actual buildings and conducted the post-occupancy evaluation.