Time-dependent photovoltaic performance assessment on a global scale using artificial neural networks

The integration of Renewable Energy Sources (RESs), particularly solar PhotoVoltaics (PVs) has become an imperative aspect of sustainable energy systems. In this pursuit, accurate and efficient simulation tools play a pivotal role in optimizing the performance of PV systems. Traditional simulation approaches, while effective, are often characterized by computational complexities and time-intensive processes. This paper introduces a groundbreaking paradigm in solar energy modeling by harnessing the power of Artificial Neural Networks (ANNs) to revolutionize the accuracy and reliability of PV system simulations. In this work, an hourly, daily, monthly and yearly comparison of the electrical energy obtained with the 5-parameter model and those obtained with the ANNs was developed. For this purpose, a very wide ensemble of localities around the world and types of PV systems were considered in the training and validation phase. ANNs exhibited a maximum mean absolute relative error of 3.5% during training and consistently maintained hourly relative errors below 5% across diverse localities during validation. Hourly power forecasting remains acceptable also in localities with extreme weather conditions. Monthly errors peak at high negative and positive latitudes in summer months when daylight duration exceeds nighttime. However, in the least accurate locality, yearly energy forecasting yielded a maximum error of 8%. Empirical equations based on the trained ANNs are proposed and a relative input-output importance criterion was applied to detect the impact of air temperature and solar radiation on the performance of each PV module. The proposed ANNs demonstrate significant utility in decision-making and real-time processes, providing a valuable framework for managing energy flows within a network and predicting energy production during specific time intervals. This alternative approach surpasses conventional dynamic simulation methodologies found in existing literature in terms of computational cost with comparable accuracy

Forecasting green roofs’ potential in improving building thermal performance and mitigating urban heat island in the Mediterranean area: An artificial intelligence-based approach

Green roofs are widely used in hot or cold climates mainly because they are capable to improve the energy efficiency of buildings and, when implemented at a large scale, reducing air pollution and the urban heat island effect (UHI) in urban contexts. Artificial Neural Network (ANN) black-box algorithms are a valid alternative to studying complex systems. However, the literature highlights - quite surprisingly – none of the available research refers to coupling ANNs and green roofs in the Mediterranean area, where green roofs are instead considered one of the most suitable technologies to reduce the high cooling demand. Therefore, the objective of this research work is to create and validate an ANN for the prediction of the monthly green roof’s internal and external surface temperatures and the monthly internal air temperature, starting from different green roof parameters and climatic variables. Specifically, the ANN was created with reference to a Mediterranean climate considering an existing green roof on a building of the University of Palermo characterized by a cooling demand predominance; 180 green roof configurations, obtained by varying the characteristic parameters of vegetation (plant height, leaf area index and leaf reflectivity) and the substrate thickness and thermophysical properties (lightweight and heavyweight), were dynamically simulated on an hourly basis to build the training dataset. In addition, other 72 green roof configurations were simulated to generate the dataset for the validation purpose of the ANN accuracy. The optimal ANN-related architecture consists of 90 neurons with one hidden layer and guarantees very high accuracy predictions. The outcomes of this research represent a useful tool to determine the thermal response of green roofs and their impact on the building energy demand and indoor thermal comfort and UHI mitigation