论文信息 - Analytics for Smarter Buildings

Analytics for Smarter Buildings

Buildings consume about 40% of the total energy in most countries contributing to a significant amount of greenhouse gas (GHG) emissions and global warming. Therefore, reducing energy consumption in buildings, making buildings more energy efficient and operating buildings in more energy efficient manner are important tasks in today’s world. Analytics can play an important role in identifying energy saving opportunities in buildings by modeling and analyzing how energy is consumed in buildings. In this paper, a set of analytics which can assist building owners, facility managers, operators and tenants of buildings in assessing, benchmarking, diagnosing, tracking, forecasting, simulating and optimizing energy consumption is presented. Analytics for Smarter Buildings

[1] Francesco Longo,et al. Modeling Energy Portfolio Scoring: A Simulation Framework , 2015 .

[2] Alberto Hernandez Neto,et al. Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption , 2008 .

[3] Refrigerating. ASHRAE handbook of fundamentals , 1967 .

[4] James E. Braun,et al. An Inverse Gray-Box Model for Transient Building Load Prediction , 2002 .

[5] Lakhmi Jain,et al. Computational Economics: A Perspective from Computational Intelligence , 2006 .

[6] Joe White,et al. Performance Management Through Societal Performance Indicators , 2011, Int. J. Bus. Intell. Res..

[7] Manzur Murshed,et al. From Tf-Idf to learning-to-rank: An overview , 2016 .

[8] Guy R. Newsham,et al. Building-level occupancy data to improve ARIMA-based electricity use forecasts , 2010, BuildSys '10.

[9] Young M. Lee,et al. IBM Research Report Statistical Modeling for Anomaly Detection, Forecasting and Root Cause Analysis of Energy Consumption for a Portfolio of Buildings , 2011 .

[10] James E. Braun,et al. EFFICIENT AND ROBUST TRAINING METHODOLOGY FOR INVERSE BUILDING MODELING , 2012 .

[11] Keith A. Woodbury,et al. Inverse problems and parameter estimation: integration of measurements and analysis , 1998 .

[12] Thilini Ariyachandra,et al. Competing with BI and Analytics at Monster Worldwide , 2011, 2011 44th Hawaii International Conference on System Sciences.