Affiliations: [a] BEARS, University Town, NUS Campus, Singapore
| [b] Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh, Saudi Arabia
| [c] Saudi Electricity Company Chair in Power System Reliability and Security, King Saud University, Riyadh, Saudi Arabia
| [d] Deparment of Electrical Engineering, College of Engineering, Majmaah University, Riyadh, Saudi Arabia
Correspondence:
[*]
Corresponding author. Abdulaziz Almutairi, Deparment of Electrical Engineering, College of Engineering, Majmaah University, Riyadh, Saudi Arabia. E-mail: [email protected].
Abstract: The electric load forecasting (ELF) is a key area of the modern power system (MPS) applications and also for the virtual power plant (VPP) analysis. The ELF is most prominent for the distinct applications of MPS and VPP such as real-time analysis of energy storage system, distributed energy resources, demand side management and electric vehicles etc. To manage the real-time challenges and map the stable power demand, in different time steps, the ELF is evaluated in yearly, monthly, weekly, daily, and hourly, etc. basis. In this study, an intelligent load predictor which is able to forecast the electric load for next month or day or hour is proposed. The proposed approach is a hybrid model combining empirical mode decomposition (EMD) and neural network (NN) for multi-step ahead load forecasting. The model performance is demonstrated by suing historical dataset collected form GEFCom2012 and GEFCom2014. For the demonstration of the performance, three case studies are analyzed into two categories. The demonstrated results represents the higher acceptability of the proposed approach with respect to the standard value of MAPE (mean absolute percent error).
Keywords: Feature extraction, decomposition, intelligent data analytics, short-term forecasting, power system planning
