| KEYWORD |
Use of Machine Learning algorithms for the optimization of the energy management of photovoltaic systems
keywords MACHINE LEARNING, ARTIFICIAL NEURAL NETWORKS, PHOTOVOLTAIC SYSTEMS, SOLAR CELLS
Reference persons ANDREA LANZINI
Research Groups Energy Center Lab, M3ES
Thesis type SPERIMENTALE E SIMULAZIONE
Description The spread of measurement and monitoring systems and the advent of the Internet-of-Things guarantees an ever greater availability of data. Machine Learning algorithms are among the most effective and current tools for analyzing this data. The potential applications of these techniques, in relation to the optimization of energy management, range from the extraction of useful knowledge, to the forecast of future consumption / production values, up to the identification of any anomalies. In the context of the management of plants for the production of renewable energy, and in particular with regard to photovoltaics, the use of Machine Learning can be a fundamental tool in support of a new paradigm of maintenance of predictive plants, as well as of an optimal economic management of the same.
Required knowledge: knowledge of the theoretical bases of solar production and of the operating principles of photovoltaic panels. More in-depth knowledge of photovoltaic systems and the phenomena of damage to the panels or reduction of the efficiency of a system may be useful but are not a prerequisite. Knowledge of the Python programming language is recommended.
Deadline 01/10/2022
PROPONI LA TUA CANDIDATURA