Paper
Causal Inference in Energy Demand Prediction
Authors
Chutian Ma, Grigorii Pomazkin, Giacinto Paolo Saggese, Paul Smith
Abstract
Energy demand prediction is critical for grid operators, industrial energy
consumers, and service providers. Energy demand is influenced by multiple
factors, including weather conditions (e.g. temperature, humidity, wind
speed, solar radiation), and calendar information (e.g. hour of day and
month of year), which further affect daily work and life schedules. These
factors are causally interdependent, making the problem more complex than
simple correlation-based learning techniques satisfactorily allow for. We
propose a structural causal model that explains the causal relationship
between these variables. A full analysis is performed to validate our causal
beliefs, also revealing important insights consistent with prior studies.
For example, our causal model reveals that energy demand responds to
temperature fluctuations with season-dependent sensitivity. Additionally, we
find that energy demand exhibits lower variance in winter due to the
decoupling effect between temperature changes and daily activity patterns.
We then build a Bayesian model, which takes advantage of the causal insights
we learned as prior knowledge. The model is trained and tested on unseen
data and yields state-of-the-art performance in the form of a 3.84 percent MAPE on
the test set. The model also demonstrates strong robustness, as the
cross-validation across two years of data yields an average MAPE of 3.88 percent.