To develop a machine learning model that predicts the short-term electricity load in Hoboken based on historical data and influencing factors.

Electric load forecasting is the process of predicting the future electricity consumption or demand for a particular region or system over a specific time period. This is necessary for utility companies to efficiently manage the generation, transmission, and distribution of electricity. Electricity demand is affected by factors such as weather, and time of day.

• Data Collection: PJM Interconnection is a regional transmission organization (RTO) that oversees the flow of electricity through high voltage transmission lines in 13 states in the US. Hoboken, NJ is part of the PJM region and historical load data would be obtained from their website.

• Exploratory Data Preprocessing: The data would undergo cleansing to replace defective and missing values 

• Data Analysis: The trends would be identified and visualized to select the appropriate machine learning algorithm.

• Model Selection

• Training and Testing using the data

• Implementation using real-time predictions

It is expected that the selected machine learning algorithm would be able to accurately forecast the electricity demand in Hoboken.

Assuming that there would be available datasets at my disposal, this project should not have any monetary costs attached.