Integration of Waste Management Companies in Micro Grids Through Machine Learning

Waste management companies are one of the major contributors in emission of greenhouse gases. In order to reduce this, we need to integrate renewable energy resources with these waste management companies. These waste management companies have a very high energy consumption processes involved in their operations. But most of these companies also rely on local power generation and hence making it a beneficial field to study micro grids. Our main aim through this research is to make sure that these waste management companies work with their best efficiency by processing the waste and to enable it dominance on micro grids without elimination user interference. This is done through a very unique machine learning approach. In this approach we first predict the amount of waste that can be brought to these companies with the help of ANN (artificial neural network). Then this waste is processed and optimized through Lagrangian Algorithm on the energy requirements that are dependent upon renewable energy. Also, this approach caters to user interference and preferences. This user preference is fed to the model with the help of user feedbacks and then processed through support vector machine. In order to include user feedback, we have used an active learning paradigm. This method is tested on a waste management company of district Warendorf. The results hence prove that the energy requirements can be managed and controlled in a micro grid with giving importance to user feedback.