(Nazarbayev University School of Science and Technology, 2017-05) Sterling, Gulnaz
Energy management in smart buildings and homes has become an important issue.
Proper energy management is judged upon the amount of consumed electrical energy
as well as the total electricity cost. In this master thesis, two optimization algorithms,
namely Action Dependent Heuristic Dynamic Programming (ADHDP) and Genetic
Algorithms (GA) are used for the energy resource scheduling problem. The main
objective of the renewable energy resource scheduling problem is to decrease the
electricity cost over a fixed time period while meeting demand. In this work, ADHDP
and GA were trained and evaluated on different simulation scenarios with various
amounts of available renewable energy. It was demonstrated by computer simulations
that both ADHDP and GA are effective in cost minimization compared to the baseline
method. A correlation between optimization improvement and available renewable
energy was also confirmed by computer simulation in all scenarios.