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PhotoVoltaic (PV) plants can provide important economic and environmental benefits to electric systems. On the other hand, the variability of the solar source leads to technical challenges in grid management as PV penetration rates increase continuously. For this reason, PV power forecasting represents a crucial tool for uncertainty management to ensure system stability. In this paper, a novel hybrid methodology for the PV forecasting is presented. The proposed approach can exploit clear-sky models or an ensemble of artificial neural networks, according to day-ahead weather forecast. In particular, the selection among these techniques is performed through a decision tree approach, which is designed to choose the best method among those aforementioned. The presented methodology has been validated on a real PV plant with very promising results.

Building energy demand accounts for an important part of global energy consumption and emissions. Load forecasting is a crucial functionality for building management systems (BMSs) in order to optimize the energy supply and use with the goal of achieving economic and environmental benefits. Furthermore, if the building is a sports/event venue (one of the most energy-intensive facility type), load forecasting acquires an even greater importance. This paper presents a new forecasting hybrid technique that combines an ensemble of Artificial Neural Networks (ANNs) with an AutoRegressive Integrated Moving Average with eXternal inputs (ARIMAX) model. The proposed methodology is designed for the 24-hour-ahead electrical consumption forecasting of sports venues. These types of buildings are generally characterized by large fluctuations in their load caused by the hosted events, and therefore they are challenging for forecasting tools. The ANNs ensemble is composed of the average output of several multi-layer perceptrons, while the ARIMAX is used to correct the prediction provided by the ANNs working on the ensemble residual. The proposed approach can be easily implemented within a BMS that can be crucial for an efficient facility management. The hybrid methodology has been tested and validated on the load data of a real football arena. The proposed methodology achieves a MAPE of 9.03 % on an open dataset, and 9.38% on a closed dataset, outperforming standard machine learning techniques, hybridization techniques involving time-series and machine learning methods, as well as a state of the art event venue load forecasting technique. Furthermore, a feature importance study reveals that all the selected inputs are crucial in achieving the result. Finally, the inclusion of results related to an open dataset encourages further studies and comparisons. • An approach to load forecasting specifically tailored to stadiums. • A novel hybrid technique involving an ensemble of ANNs combined with an ARIMAX. • Experiments on open dataset of a stadium in Norway to facilitate reproducibility. • Comprehensive comparison with standard and state-of-the-art benchmarks. • A feature importance study on the selected inputs. [ABSTRACT FROM AUTHOR]

PhotoVoltaic (PV) plants can provide important economic and environmental benefits to electric systems. On the other hand, the variability of the solar source leads to technical challenges in grid management as PV penetration rates increase continuously. For this reason, PV power forecasting represents a crucial tool for uncertainty management to ensure system stability. In this paper, a novel hybrid methodology for the PV forecasting is presented. The proposed approach can exploit clear-sky models or an ensemble of artificial neural networks, according to day-ahead weather forecast. In particular, the selection among these techniques is performed through a decision tree approach, which is designed to choose the best method among those aforementioned. The presented methodology has been validated on a real PV plant with very promising results. [ABSTRACT FROM AUTHOR]