کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6733834 504061 2014 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Measurement and verification of building systems under uncertain data: A Gaussian process modeling approach
ترجمه فارسی عنوان
اندازه گیری و تایید سیستم های ساختمان با داده های نامشخص: یک روش مدل سازی فرایند گاوسی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی انرژی های تجدید پذیر، توسعه پایدار و محیط زیست
چکیده انگلیسی
Uncertainty in sensor data (e.g., weather, occupancy) complicates the construction of baseline models for measurement and verification (M&V). We present a Monte Carlo expectation maximization (MCEM) framework for constructing baseline Gaussian process (GP) models under uncertain input data. We demonstrate that the GP-MCEM framework yields more robust predictions and confidence levels compared with standard GP training approaches that neglect uncertainty. We argue that the approach can also reduce data needs because it implicitly expands the data range used for training and can thus be used as a mechanism to reduce data collection and sensor installation costs in M&V processes. We analyze the numerical behavior of the framework and conclude that robust predictions can be obtained with relatively few samples.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Energy and Buildings - Volume 75, June 2014, Pages 189-198
نویسندگان
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