Abstract:
In this thesis work, a new technique is proposed to forecast short term electrical load. Load
forecasting is an integral part of power system planning and operation. Precise forecasting of
load is essential for unit commitment, capacity planning, network augmentation and demand
side management. It is significantly imperative for energy providers and other members in
electric energy generation, transmission, distribution and markets. Forecasting of load
demand is a complex problem as it is to solve nonlinearity with influenced external factors.
Load forecasting can be generally categorized into three classes such as sort term, midterm
and long term. Short term forecasting is usually done to predict load for next few hours to
few weeks. In the literature various methodologies such as regression analysis, machine
learning approaches, deep learning methods and artificial intelligence systems have been
used for short term load forecasting. Existing forecasting techniques may not always provide
higher accuracy in short term load forecasting. To overcome this challenge, a new approach
is proposed in this thesis for short term load forecasting. The developed method is based on
the integration of convolutional neural network and long short-term memory network. The
method is applied to Bangladesh power system to provide day ahead forecasting to month
ahead. It is found that in the field of short-term load forecasting, the proposed strategy
results in higher precision and accuracy in terms of Mean average error (MAE), Mean
average percentage error (MAPE) and root mean square error (RMSE).
