SHORT TERM LOAD FORECASTING TECHNIQUE BASED ON INTEGRATION OF CONVOLUTIONAL NEURAL NETWORK AND LONG-SHORT-TERM MEMORY NETWORK

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).