These schemes are based on Genetic Algorithms and Quasi-Newton Algorithms in MATLAB, implementing Resistance-Capacitance (RC) Analogy for Grey-Box Thermal Modelling and Hybrid Optimization of a Residential House.
Here’s an abstract of a research paper is submitted for eSim conference to be held at Vancouver, BC: In this research, a grey-box modelling by implementing Resistance-Capacitance (RC) analogy was developed to estimate the thermal demand of a residential house. The actual specifications of a research house in Vaughan, Ontario, Canada was used as the model inputs. Two parallel short- and long-term calibrations were performed such that model outputs reflect the real-world operation of the house as best as possible. To define the unknown model parameters, a hybrid optimization scheme including genetic algorithms (GA) and Quasi-Newton algorithm was introduced and implemented. The final iteration of the model achieved a root-mean-square error for interior zone temperature estimation of 0.26. Furthermore, the annual heating and cooling consumption from the eventual model simulation were reported. According to these preliminary results, the introduced model and optimization techniques could be adjusted for different houses as well as for smart control applications on both short- and long-term basis.
Design and Field Implementation of Blockchain Based Renewable Energy Trading in Residential Communities, Journal article(EESS). https://arxiv.org/abs/1907.12370