Josivan Leite Alves

Practical Applications This study investigated how artificial intelligence models can enhance solar energy forecasting in diverse regions. By using data collected over time, the research compared the performance of three different predictive models to identify which offers the most accurate results. Among them, the model based on a feedforward neural network, a type of machine-learning approach, stood out for producing the lowest error rates in most of the locations studied.

You are already at the latest version Building integrated photovoltaic systems (BIPV) is a strategy to achieve energy self-sufficiency in buildings. However, photovoltaic (PV) energy production presents challenges due to its intermittent nature, characterized by variations and uncertainties associated with solar radiation and interference from the building's surroundings. Therefore, BIM enables energy simulations and solar performance analyses during the design phase of buildings.

th International Conference on Software Process Improvement, CIMPS 2018. (2019). Advances in Intelligent Systems and Computing, Vol. 865. Retrieved from https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054840742&partnerID=40&md5=41456418eecbaed2ec251cf2abec17df Ahmad, R., Masse, C., Jituri, S., Doucette, J., & Mertiny, P. (2018). Alberta Learning Factory for training reconfigurable assembly process value stream mapping. Procedia Manufacturing, 23, 237–242.