Maximizing Solar Output Power: Load Shedding Design Approach

Renewable energy is currently at the centre of meeting the world energy needs. A notable number of offices and home-users have employed the renewable energy technologies, which are usually used for power backup purposes. High cost of generation is the major factor militating against solar power system affordability. Therefore, an effective way of maximizing the limited generated solar power is paramount. This research presents a viable method of minimizing the overall cost of implementing a solar power system by adequate management of the load to be powered. This paper presents a load shedding design approach in maximizing the solar output power usage in meeting the ever dynamic power need in a typical office setting. The estimated cost of implementing the full load design was compared side by side with a proposed load shedding design approach. In addition, the shed loads were carefully considered to ensure the office day to day activities were not hindered. The load shedding design was found to reduce the total effective load of the office complex. It also reduced the total cost of implementing the solar power design to meet a required load by 90.87%. Keywords: Solar Power, Maximize, Load Shedding, Renewable Energy, Full Loa

TEXT CONTENT DEPENDENT WRITER IDENTIFICATION

Text content based personal Identification system is vital in resolving problem of identifying unknown document’s writer using a set of handwritten samples from alleged known writers. Text written on paper document is usually captured as image by scanner or camera for computer processing. The most challenging problem encounter in text image processing is extraction of robust feature vector from a set of inconstant handwritten text images obtained from the same writer at different time. In this work new feature extraction method is engaged to produce active text features for developing an effective personal identification system. The feature formed feature vector which is fed as input data into classification algorithm based on Support Vector Machine (SVM). Experiment was conducted to identify writers of query handwritten texts. Result show satisfactory performance of the proposed system, it was able to identify writers of query handwritten texts