Power System Load Modeling Using A Weighted Optimal Linear Associative Memory (Olam)

Abstract

Power system load models are very powerful tools, which have a wide range of applications in the electric power industry. These uses include scheduling system maintenance, monitoring load management policies, helping with the generator commitment problem by providing short-term forecasts, and aiding system planning [4]. Further, Power System Load Modeling is a technique used to model a power system and other essentials for the assessment of stability. In today\u27s datacenters, power consumption is a major issue. Storage usually typically comprises a large percentage of a datacenter\u27s power. Therefore, without mentioning that managing, understanding, and reducing storage, power consumption is an essential aspect of any efforts that address the total power consumption of datacenters. Moreover, according to [16], power system load models have a wide range of applications in the electric power industry including load management policy monitoring, such as aiding with system planning by providing long-term forecasts, short-term forecasts, and others including assisting with the generator commitment problem. The direct impact that population growth and technological development have on the electric demand load cannot be under estimated. This thesis partly served as a reminder that through data and research that the direct proportional relationship between xiii population growth and demand load, and technological development and demand load makes up the entire concept of electric power generation and the entire electric power system that is a part of our daily lives. Since they are a part of our daily lives, power system engineers should and must derive mathematical models, namely, Traditional Least Squares, Truncated Fourier series, the use of artificial neural networks, and the Optimal Linear Associative Memory (OLAM) to capture these impacts on demand load