Date of Award
Master of Science in Electrical Engineering (MSEE)
Electrical and Computer Engineering
Dr. Yu Chen
With the development of modern information and technology (IT), smart grids became one of the major components of smart cities, to take full advantage of the smart grid, the capability of intelligent scheduling and planning of electricity delivery is essential. For this purpose, researchers have investigated methodologies for power consumption prediction and demand side management (DSM). In addition, conducting a comprehensive analysis and obtaining an accurate evaluation of power consumption are the premise and basis for a more robust and efficient power grid design and transformation. Therefore, it is meaningful to explore forecasting models that are able to reflect the power consumption change effectively.
Making electricity consumption prediction based on neural network has been a popular research topic in recent years, and backpropagation neural network (BPNN) algorithm has been recognized as a mature and effective method. This thesis applies the BPN to predict the electricity consumption of Pecan Street, a community with a relatively large scale smart grid, and takes more factors into account, such as weather condition, weekend and holiday. The influences of each factor have been evaluated for a deeper insight. While what presented in this thesis is not mature, it may inspire more discussion and further study to guide the design of future smart grids.
Song, Hao, "USING MULTIFACTOR INPUTS BP NEURAL NETWORK TO MAKE POWER CONSUMPTION PREDICTION" (2018). Graduate Dissertations and Theses. 82.