Adoption of Smart Grid in Ghana Using Pattern Recognition Neural Networks

Abstract

Deployment of Smart Grid is neither a goal nor a destination, but rather an enabler to the provision of reliable, secured and clean electricity for the end-user or consumer. Overall Smart Grid vision is very well explained with the future of electricity systems, which largely depends on digitization and automation of the overall electricity value-chain, by enhancing electric power information to bi-directional flow and the provision of services that can support the operations of the generation, distribution and end-user usage of power can lead to improvement of electric power system efficiency. This work aims at analyzing factors and forecast effects on the adoption of Smart Grid in Ghana using Pattern Recognition Neural Net. The Primary data was collected using structured questionnaire and the questions were designed to test the perception of consumers on the deployment of Smart Grid. Also, the target group of respondents covered 80% of the regions in Ghana. Based on the collected data, the pattern recognition neural networks was employed in the analysis of data. Results indicated that education, government policy, cost and safety were the main drivers to the deployment of Smart Grid in Ghana. Other drivers like culture and societal perception recorded as insignificant variables to the deployment of distributed generation in Ghana. It is recommended that further research work should examine the extent of infrastructural preparedness of Ghana for the deployment of Smart Grid.