Abstract:
Short Term Load Forecasting (STLF) aims at predicting power consumption in the hours, days, or weeks ahead. Accurate STLF is important for plant
scheduling, financial planning, system security, short-term maintenance, short term storage usage, and the application of demand response strategies, which
aim at rewarding reduced power consumption at peak hours. State of the art
work uses single task deep learning (STL) for its ability to model the uncertainties in the individualized load. However, the approach can be improved further
by combining data from other smart meters. To advance STLF accuracy, this
thesis explores the use of transfer learning. Two new transfer learning models
are proposed: A hierarchical clustering with population STLF prediction models
(HC-P) and a hierarchical clustering with deep multitask learning (HC-MTL).
Each of the two hierarchical algorithms cluster similar smart meters into groups
based on smart meters’ data representation. The HC-P approach allows smart
meters in each group to learn a shared feature representation. The HC-MTL
approach uses hard parameter sharing (HPS) schemes for smart meters in the
same group and soft parameter sharing (SPS) for smart meters that are unique
and different from all other smart meters. The thesis’s additional contributions
include two studies for deeper insights into the limitations and opportunities of
using transfer learning for STLF. The first study examines the effect of available
data on STLF accuracy. The study shows that more smart meter training data
helps in improving STLF accuracy, but up to a certain saturation point beyond
which, limited performance gains can be obtained. This insight suggests that
transfer learning will only make a difference for STLF of smart meters that do
not have sufficient training data. To confirm the benefit of transfer learning, a
second study examined the effect of getting more data by way of adding data
from similar smart meters. Finally, to evaluate the proposed transfer learning
STLF approaches HC-P and HC-MTL, experiments were conducted on a dataset
consisting of 4225 residential smart-meters and 484 industrial smart meters. Several models were implemented for comparison, including: the state of art STL
model composed of 1D-Convolutional Neural Network (CNN) with Gated Recurrent Unit (CNN-GRU), a population prediction model without grouping, a
hierarchical random grouping with population prediction models, Autoregressive
Integrated Moving Average (ARIMA), and Seasonal ARIMA (SARIMA). The
results showed that the HC-P worked best for residential smart meters’ STLF
and HC-MTL worked best for industrial STLF. In fact, compared to prior state of-the-art, HC-P provided an accuracy improvement of 2% RMSE and MAE for
residential smart meters. For industrial smart meters, HC-MTL provided an
improvement of 2.78% and 4.97% in terms of RMSE and MAE, respectively