{'fraction_non_alphanumeric': 0.014194464158977998, 'fraction_numerical': 0.0042583392476933995, 'mean_word_length': 5.077586206896552, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Thermal Dynamic Modeling for Home Energy Management : A Case Study

The modeling of power system loads is a very important problem since loads are ultimately the driving force behind the entire system. In this report, we consider the problem of thermal dynamics air conditioner load models for residential homes. In particular, we investigate a physical-based load modeling methodology. We propose a method using minimization of the least square error to obtain the essential parameters α, G, and c from the linear differential equation in which the indoor temperature represents the state of energy storage in the house. The essential parameters describe the energy balance for a single house.The data used for this study corresponds to real measurements taken from a house and it was provided by Intel. The data consists of indoor and outdoor temperatures as well as HVAC power consumption for the different compartments of the house. Upon experiments that includes computational simulation, extensive data analysis, it is concluded that 1 day of training samples for parameter prediction performs reasonable well in comparison of 7 and 14 days. The validation of the model is performed using data in two different months. The modeling and prediction mean squared error obtained are below 0.1 degree. These results open the possibility to investigate techniques to incorporate adaptive temperature learning.