Accurate net load forecasting is a cost-effective technique, crucial for the planning, stability, reliability, and integration of variable solar photovoltaic (PV) systems in modern power systems. This work presents a direct short-term net load forecasting (STNLF) methodology for solar-integrated microgrids by leveraging machine learning (ML) principles. The proposed data-driven method comprises of an initial input feature engineering and filtering step, construction of forecasting model using Bayesian neural networks, and an optimization stage. The performance of the proposed model was validated on historical net load data obtained from a university campus solar-powered microgrid. The results demonstrated the effectiveness of the model for providing accurate and robust STNLF. Specifically, the optimally constructed model yielded a normalized root mean square error of 3.98% when benchmarked using a 1-year historical microgrid data. The k -fold cross-validation method was then used and proved the stability of the forecasting model. Finally, the obtained ML-based forecasts demonstrated improvements of 17.77% when compared against forecasts of a baseline naïve persistence model. To this end, this work provides insights on how to construct high-performance STNLF models for solar-integrated microgrids. Such insights on the development of accurate STNLF architectures can have positive implications in actual microgrid decision-making by utilities/operators.

This article describes a complete characterization system for photovoltaic devices designed to acquire the current-voltage curve and to process the obtained data. The proposed system can be replicated for educational or research purposes without having wide knowledge about electronic engineering. Using standard calibrated instrumentation, commonly available in any laboratory, the accuracy of measurements is ensured. A capacitive load is used to bias the device due to its versatility and simplicity. The system includes a common part and an interchangeable part that must be designed depending on the electrical characteristics of each PV device. Control software, developed in LabVIEW, controls the equipment, performs automatic campaigns of measurements, and performs additional calculations in real time. These include different procedures to extrapolate the measurements to standard test conditions and methods to obtain the intrinsic parameters of the single diode model. A deep analysis of the uncertainty of measurement is also provided. Finally, the proposed system is validated by comparing the results obtained from some commercial photovoltaic modules to the measurements given by an independently accredited laboratory.