Load forecasting is a critical aspect for power systems planning, operation and control. In this paper, as part of research efforts of an ambitious project at Memorial University of Newfoundland in St. John's, Canada, to achieve more energy efficient and environmental friendly ''Sustainable Campus'', we present a day-ahead load forecasting approach for the energy management system of the project. The hourly load consumption dataset from January 1, 2016 to March 31, 2020 is used in the paper, which was collected from two power meters on campus. Using the load consumption dataset along with the collected meteorological dataset, a total of 19 regression model-based day-ahead load forecasting algorithms for Memorial University of Newfoundland's campus load are developed and evaluated in this paper. These 19 models belong to five families of regression models in MATLAB Regression Toolbox: Linear Regression, Regression Trees, Support Vector Machines (SVM), Gaussian Process Regression (GPR), and Ensemble of Trees. It is found that the family of GPR models shows the best load forecasting performance because they are nonparametric kernel-based probabilistic models. Two GPR models, Rational Quadratic GPR and Exponential GPR, are recommended as the best models for load forecasting through this study.INDEX TERMS Short-term load forecasting, regression model, day-ahead load forecasting, Gaussian process regression, probabilistic models, university campus load.

Photovoltaic (PV) systems have recently been recognized as a leading way in the production of renewable electricity. Due to the unpredictable changes in environmental patterns, the amount of solar irradiation and cell operating temperature affect the power generated by the PV system. This paper, therefore, discusses the grid-integrated PV system to extract maximum power from the PV array to supply load requirements and the supply surplus power to the AC grid. The primary design is to have maximum power point tracking (MPPT) of the non-uniformly irradiated PV array, conversion efficiency maximization, and grid synchronization. This paper investigates various MPPT control algorithms using incremental conductance method, which effectively increased the performance and reduced error, hence helped to extract solar array’s power more efficiently. Additionally, other issues of PV grid-connected system such as network stability, power quality, and grid synchronization functions were implemented. The control of the voltage source converter is designed in such a way that PV power generated is synchronous to the grid. This paper also includes a comparative analysis of two MPPT techniques such as incremental conductance (INC) and perturb-and-observe (P&O). Extensive simulation of various controllers has been conducted to achieve enhanced efficient power extraction, grid synchronization and minimal performance loss due to dynamic tracking errors, particularly under fast-changing irradiation in Matlab/Simulink. The overall results favour INC algorithm and meet the required standards.