Grid Frequency Forecasting in University Campuses using Convolutional
  LSTM
Aneesh Sathe Wen Ren Yang
The modern power grid is facing increasing complexities, primarily stemming
from the integration of renewable energy sources and evolving consumption
patterns. This paper introduces an innovative methodology that harnesses
Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks
to establish robust time series forecasting models for grid frequency. These
models effectively capture the spatiotemporal intricacies inherent in grid
frequency data, significantly enhancing prediction accuracy and bolstering
power grid reliability. The research explores the potential and development of
individualized Convolutional LSTM (ConvLSTM) models for buildings within a
university campus, enabling them to be independently trained and evaluated for
each building. Individual ConvLSTM models are trained on power consumption data
for each campus building and forecast the grid frequency based on historical
trends. The results convincingly demonstrate the superiority of the proposed
models over traditional forecasting techniques, as evidenced by performance
metrics such as Mean Square Error (MSE), Mean Absolute Error (MAE), and Mean
Absolute Percentage Error (MAPE). Additionally, an Ensemble Model is formulated
to aggregate insights from the building-specific models, delivering
comprehensive forecasts for the entire campus. This approach ensures the
privacy and security of power consumption data specific to each building.