# Your project title # Your name Due on November 23, Monday at 11:59 PM. The final project is an individual project, where you apply one or more machine learning techniques to some power system...

The assignment is a final class project. Please see attached PDF file and data.


# Your project title # Your name Due on November 23, Monday at 11:59 PM. The final project is an individual project, where you apply one or more machine learning techniques to some power system planning/operation problem. A good project either has the potential to be polished into a research paper, or is suitable to be transformed into teaching materials. I will give you some ideas in the lectures on October 26. This template is for your reference. You do not have to strictly follow it. You can remove the instructions. You need to submit a .zip file, which includes the .Rmd file, the .html file, a data folder, and a figs folder (if applicable). The project will be graded by both the instructor and the TA. Please feel free to contact (at least one of) us (by email or attending office hours) as you work on the project. The outline of the project is similar to an IEEE research paper. Introduction Do not include a reference list in the appendix. Instead, include a link to cite a reference, e.g., a benchmark model is developed in [Hong11], another model is developed in [Xie18]. Problem Statement Formulate a problem in power systems from the data analytics/machine learning perspective. Clearly specify the available data sets, the objective, the performance metric, and the state of the art (best performance in the current literature for the same or a similar problem). You can load all the needed packages here and have a glance at the data: library(tidyverse) bse <- read_csv("data/bse_clean.csv",="" col_types="cols(Hour" =="" col_factor()))="" %="">% print() ## # A tibble: 51,192 x 4 ## Date Hour Load T ## ## 1 2003-03-01 1 12863 23 ## 2 2003-03-01 2 12389 22 ## 3 2003-03-01 3 12155 21 ## 4 2003-03-01 4 12072 21 ## 5 2003-03-01 5 12160 22 ## 6 2003-03-01 6 12568 21 ## 7 2003-03-01 7 13236 22 ## 8 2003-03-01 8 14190 22 ## 9 2003-03-01 9 15213 24 ## 10 2003-03-01 10 15647 27 ## # … with 51,182 more rows You can include code for data preprocessing in either this section or later sections. https://doi.org/10.1109/PES.2011.6038881 https://doi.org/10.1007/s40565-017-0374-0 Methodology Describe your proposed methods (e.g., a classical machine learning method and a deep neural network). Intuitively argue why your methods may be better than the existing methods (in some aspects, of course). The instructor and the TA are aware that some topics may be more sophisticated, and even reproducing the state of the art is already challenging enough (which is acceptable in this project; we will take into account all the factors and grade your work based on the overall quality). You can include a figure like this: You can create a math equation like this: \[ E = mc^2. \] You can include some key code in this section to highlight the main idea and contributions of your work. Results Most of your code is included here, with the generated results. You may want to compare your results with the state of the art, and discuss the results. Conclusion Main takeaways of your work, and future directions of your work.