Learning Peaks for Commercial and Industrial Electric Loads

As on 2017, US Energy Information Administration (US EIA) claims that 50 % of the total US energy consumption are contributed by Commercial and Industrial (C&I) end-users.

Most of the energy consumption by these users is in the form of the electric power. Electric utilities, who usually supply the electric power, tend to care about the power consumption profiles of these users mainly because of the scale of consumption and their significant contribution

towards the system peak. Predicting and managing the peaks of C&I users is crucial both for the users themselves and for utility companies.

In this research, we aim to understand and predict the daily peaks of individual C&I users. To empirically understand the statistical characteristics of the peaks, we perform an extensive exploratory data analysis using a real power consumption time series dataset. To accurately predict the peaks, we investigate indirect and direct learning approaches. In the indirect approach, daily peaks are identified after forecasting the entire time series for the day whereas in the direct approach, the daily peaks are directly predicted based on the historical data available for different users during different days of the week. The machine learning models used in this research are based on Simple Linear Regression (SLR), Multiple Linear Regression (MLR), and Artificial Neural Networks (ANN).