Name
Abstract | ||
|---|---|---|
Integrated generation systems are increasingly considered suitable to supply remote areas, less developed countries, and small isolated communities with power. The energy management investigated in this paper concerns a smart grid encompassing a photovoltaic park. We propose a novel cloud-distributed solution to determine the best energy dispatch, i.e., where energy is going to be used and whether to change the operating points for some consumption devices. Neural networks have been used to predict both energy production and consumption, making it possible to strategically set the activation time of loading devices and to minimize energy flow changes. Moreover, cloud computing resources make it possible to have fast and distributed computation on the big amount of data gauging power production and consumption. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TSG.2017.2718241 | IEEE TRANSACTIONS ON SMART GRID |
Keywords | Field | DocType |
Smart grids, photovoltaic, renewable energy, energy management, cloud computing, CPU computing, wavelet analysis, neural networks, parallel processing | Time series,Energy management,Smart grid,Control engineering,Energy flow,Real-time computing,Engineering,Artificial neural network,Photovoltaic system,Computation,Cloud computing,Distributed computing | Journal |
Volume | Issue | ISSN |
9 | 6 | 1949-3053 |
Citations | PageRank | References |
5 | 0.49 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giacomo Capizzi | 1 | 60 | 11.94 |
| Grazia Lo Sciuto | 2 | 42 | 8.75 |
| Christian Napoli | 3 | 201 | 24.64 |
| Emiliano Tramontana | 4 | 410 | 56.24 |