Machine learning is applied in almost every field of knowledge in recent years. Renewable energy domain now extensively uses machine learning to provide farmers and grid operators with proper and timely information about solar irradiance prediction for future planning. Connecting grids with solar energy systems has brought unprecedented shift in the power sector. Nevertheless, the intervention poses challenges including intermittency that affects the overall energy supply and demand management. Our proposed solution intends to make prediction models using Machine Learning techniques (Linear Regression, Regression Trees and Support Vector Machine) which uses the past weather data including the parameters such as temperature, wind speed, atmospheric pressure, and humidity and predicts solar irradiance for the future. The model can help the grid operators for improved supply and demand management.

Software conflicts arising because of conflicting changes are a regular occurrence and delay projects. Workspace awareness tools have been proposed to facilitate task coordination among developers, enabling them to identify potential conflicts early, while conflicts are still easy to resolve. However, these tools have limitations, as they identify conflicts after conflicts have already occurred and therefore, are unable to prevent developers’ time and effort spent in resolving the conflicts. The goal of this Ph.D. research is to: (1) characterize the distribution of conflicts, their frequency and the factors within a project that affects the distribution and frequency of conflicts, (2) design and implement a conflict minimization technique that proactively identifies potential conflicts by analyzing developers’ tasks and avoids them by scheduling tasks in a conflict minimal manner and (3) evaluate the proposed approach using historic data from OSS projects and through user evaluations. Thus far, we have implemented our approach and evaluated it with historic data from four OSS projects and through simulated data.

Software conflicts arising because of conflicting changes are a regular occurrence and delay projects. The main precept of workspace awareness tools has been to identify potential conflicts early, while changes are still small and easier to resolve. However, in this approach conflicts still occur and require developer time and effort to resolve. We present a novel conflict minimization technique that proactively identifies potential conflicts, encodes them as constraints, and solves the constraint space to recommend a set of conflict-minimal development paths for the team. Here we present a study of four open source projects to characterize the distribution of conflicts and their resolution efforts. We then explain our conflict minimization technique and the design and implementation of this technique in our prototype, Cassandra. We show that Cassandra would have successfully avoided a majority of conflicts in the four open source test subjects. We demonstrate the efficiency of our approach by applying the technique to a simulated set of scenarios with higher than normal incidence of conflicts.