Learning for Interval Prediction of Electricity Demand: A Cluster-based
  Bootstrapping Approach
Rohit Dube Natarajan Gautam Amarnath Banerjee Harsha Nagarajan
Accurate predictions of electricity demands are necessary for managing
operations in a small aggregation load setting like a Microgrid. Due to low
aggregation, the electricity demands can be highly stochastic and point
estimates would lead to inflated errors. Interval estimation in this scenario,
would provide a range of values within which the future values might lie and
helps quantify the errors around the point estimates. This paper introduces a
residual bootstrap algorithm to generate interval estimates of day-ahead
electricity demand. A machine learning algorithm is used to obtain the point
estimates of electricity demand and respective residuals on the training set.
The obtained residuals are stored in memory and the memory is further
partitioned. Days with similar demand patterns are grouped in clusters using an
unsupervised learning algorithm and these clusters are used to partition the
memory. The point estimates for test day are used to find the closest cluster
of similar days and the residuals are bootstrapped from the chosen cluster.
This algorithm is evaluated on the real electricity demand data from EULR(End
Use Load Research) and is compared to other bootstrapping methods for varying
confidence intervals.