A self-learning algorithm for estimating solar heat gain and temperature changes in a single-Family residence

作者：Omar Farhad^*; Bushby Steven T; Williams Ronald D

来源：Energy and Buildings, 2017, 150: 100-110.

DOI：10.1016/j.enbuild.2017.06.001

• Estimating solar heat gain
• Indoor temperature prediction
• Lumped capacitance model
• Overall heat transfer coefficient
• Residential energy control
• Self-learning algorithm
• Sliding window optimization
• Smart grid
• Thermal model
• Thermal time constant

To develop effective control optimization strategies for managing residential electricity consumption in a smart grid environment, predictive algorithms are needed that are simple to implement, minimize custom configuration, and provide sufficient accuracy to enable meaningful control decisions. This paper presents a self-learning algorithm for predicting indoor temperature in a single-family residence using a first-order lumped capacitance (1 R1 C) model that can be used to evaluate the consequences of energy saving or load shifting strategies on thermal comfort. The algorithm is formulated in such a way that key design details such as window size/configuration, thermal insulation, and airtightness (all major factors that affect heat loss and solar heat gain) are combined into effective parameters that can be learned from observation. This approach eliminates the need for a custom configuration for each residence.
The model was validated using experimental data from the National Institute of Standards and Technology (NIST) Net-Zero Energy Residential Test Facility. It was found that with a simple temperature decay test to determine a thermal time constant and a seven-day sliding window of training data to account for seasonal variations in other parameters, the algorithm can reliably predict indoor temperatures for a 24h period using a solar irradiance forecast, an outdoor air temperature forecast, and heat pump output. The average root mean square temperature prediction error was found to be 2.2%. Published by Elsevier B.V.

• 出版日期2017-9-1