Achieving CO2 emission targets for energy consumption at Canadian manufacturing and beyond; using Hybrid Optimization Model

Abstract

Due to sporadic climate change and global warming, world have signed international protocols promising to reduce their nation's emissions. This study focuses on the application of the bees algorithm, embedded with an artificial neural network, to determine practical yearly reductions for minimizing oil, natural gas, and coal emissions as by-products of energy consumption in Canada's manufacturing sector based on the Copenhagen Targets for Canada for 2020.

Scope of electrical distribution system architecture considering the integration of renewable energy in large and small scale

“…The distribution system must be aware of the demand and the external data to improve the efficiency of the grid to approach the renewable energy and to permit the reduction of cost for the consumer [17][18][19]. The exchange of data and information regarding weather, load, and generation forecasting is a requirement for an automated grid together with price optimization according the user's comfort [20].…”