DEEP LEARNING PREDICTING POWER COSTS FOR POWER PLANNING

Abstract

The current global situation, especially in Thailand, is showing recovery following the easing of COVID-19 restrictions. With a growing population and industrial expansion, there is an increased demand for electricity. This rise in demand, along with factors like fuel costs and fluctuating currency rates, has influenced global power prices. The authors are keen on studying power’s variable price prediction using machine learning techniques. The objective is to analyze factors associated with power costs and their interrelationships. It is essential to have accurate data for strategic power planning. Research has been conducted on theories related to variable power prices, time series, traditional statistical forecasting, machine learning, and deep learning. It was found that three main factors affecting variable power prices were identified: natural gas prices, exchange rates, and inflation rates, with natural gas prices and inflation rates having a strong correlation. Traditional statistical forecasting is less efficient for highly volatile power’s variable price. Deep learning models outperform conventional machine learning for this dataset.