| dc.description.abstract | Fossil fuels, a non-renewable source of energy, are gradually depleted. To cope
with the potential energy crisis on a global scale, renewable energy sources such
as solar power have been the top candidate to replace fossil fuels in energy mass
production. To become a permanent replacement, more and more studies are
required to help enterprises further reduce the production expense of solar power
plant which is still relatively high nowadays, making solar energy generation
become more applicable in many corners of the world. As machine learning
algorithm and optimization techniques are becoming more popular and applied
widely in many industries, it paves the way for the massive growth of solar power
production in the near future. This research aims to achieve two main objectives.
Firstly, a predicting model using Linear Regression technique is developed to
perform energy generation prediction. Thanks to which, the electricity supplier can
make timely decision to combat the power shortage and fulfill the demands. The
model is capable of providing reliable results with its average R-squared value of
98% and 82% respectively with two datasets. Secondly, a model under Mixed
Integer Linear Programming approach is introduced to suggest an optimal
distribution plan for the electricity vendor, with cost savings features in
consideration. Some recommendations to avoid the potential cost burden are also
made after some data adjustments. For future research, more novel machine
learning methods and weather forecast data can be explored to improve the model
accuracy and reliability, and more cost components can be added to the
optimization model to further lower the expenses. | en_US |
