6,431 research outputs found
Sense, Model and Identify the Load Signatures of HVAC Systems in Metro Stations
The HVAC systems in subway stations are energy consuming giants, each of
which may consume over 10, 000 Kilowatts per day for cooling and ventilation.
To save energy for the HVAC systems, it is critically important to firstly know
the "load signatures" of the HVAC system, i.e., the quantity of heat imported
from the outdoor environments and by the passengers respectively in different
periods of a day, which will significantly benefit the design of control
policies. In this paper, we present a novel sensing and learning approach to
identify the load signature of the HVAC system in the subway stations. In
particular, sensors and smart meters were deployed to monitor the indoor,
outdoor temperatures, and the energy consumptions of the HVAC system in
real-time. The number of passengers was counted by the ticket checking system.
At the same time, the cooling supply provided by the HVAC system was inferred
via the energy consumption logs of the HVAC system. Since the indoor
temperature variations are driven by the difference of the loads and the
cooling supply, linear regression model was proposed for the load signature,
whose coefficients are derived via a proposed algorithm . We collected real
sensing data and energy log data from HaiDianHuangZhuang Subway station, which
is in line 4 of Beijing from the duration of July 2012 to Sept. 2012. The data
was used to evaluate the coefficients of the regression model. The experiment
results show typical variation signatures of the loads from the passengers and
from the outdoor environments respectively, which provide important contexts
for smart control policies.Comment: 5 pages, 5 figure
IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation
During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture
Tensor Completion for Weakly-dependent Data on Graph for Metro Passenger Flow Prediction
Low-rank tensor decomposition and completion have attracted significant
interest from academia given the ubiquity of tensor data. However, the low-rank
structure is a global property, which will not be fulfilled when the data
presents complex and weak dependencies given specific graph structures. One
particular application that motivates this study is the spatiotemporal data
analysis. As shown in the preliminary study, weakly dependencies can worsen the
low-rank tensor completion performance. In this paper, we propose a novel
low-rank CANDECOMP / PARAFAC (CP) tensor decomposition and completion framework
by introducing the -norm penalty and Graph Laplacian penalty to model
the weakly dependency on graph. We further propose an efficient optimization
algorithm based on the Block Coordinate Descent for efficient estimation. A
case study based on the metro passenger flow data in Hong Kong is conducted to
demonstrate improved performance over the regular tensor completion methods.Comment: Accepted at AAAI 202
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