Mubarak Abu Mohsen, a researcher in the Master's program in Data Science and Business analysis, published a research paper titled "Predicting Electrical Loads Using Machine Learning Algorithms for Energy System in Palestine" in the Q1-rated journal (Energies).
The student has previously defended his thesis, which is the first in its field in Palestine, where it aimed to build an accurate, reliable model for predicting electrical loads using deep learning algorithms (LSTM, GRU and RNN), based on factual data obtained from the Tubas Electricity Company. The significance of this study stems from the importance of planning and managing the energy system to improve the processes of demanding and providing energy, transmission and distribution, which can reduce costs, enhance system reliability, mitigate environmental impacts, enhance the local economy and maintain energy sustainability.
The algorithms used in the study enabled the researcher to obtain the best results and the lowermost error rate compared to previous studies, as differed by modifying the control parameters that included the learning rate, the type of improvers, the ratio of training and testing, activation functions, lot number, size, and number of hidden layers. This all supported the reliability and credibility of the prediction system, and helped the Tubas Electricity Company in distributing electrical loads appropriately, reducing power disconnection, and contributing to planning for the improvement of the infrastructure in the governorate.
The thesis was supervised by Dr. Amani Owda and Dr. Majdi owda. Dr. Huthaifa Ashkar and Dr. Radi Jarrar participated in the thesis examination.
