9786203582444 - Planning and Operation Assessment of a Microgrid di Morad, Mohammed; Hossam E. M. S. Abbas; Ahmed I. A. Galal (9 risultati)

Condizione: New. pp. 192.

Taschenbuch. Condizione: Neu. Planning and Operation Assessment of a Microgrid | Mohammed Morad (u. a.) | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786203582444 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

paperback. Condizione: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book introduces planning and evaluation of grid-connected photovoltaic (PV) systems-based microgrid to supply Assiut University main campus with electricity. The microgrid system requires a planning policy that can anticipate how much electricity will be consumed to meet future power demands. So, the work of this book starts with the electrical energy consumption forecasting. The forecasting process, in this book, adopts two machine learning tools that are Gaussian process (GP) tool and neural networks technique. The forecasting methodology is divided into two sub-models. The first one is a neural network model in the context of nonlinear autoregressive (NAR) model that can predict future values of a set of exogenous variables affecting electrical energy consumption. The second one is a GP model, which can be trained for relating the predicted exogenous variables to the electrical energy consumption in the process of future electrical energy consumption forecasting. In this book, the GP approach has demonstrated reasonable forecasting for one year ahead with a mean absolute percentage error (MAPE) of 4.9 %. 192 pp. Englisch.

Condizione: New. Print on Demand pp. 192.

Condizione: New. PRINT ON DEMAND pp. 192.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Morad MohammedMohammed Morad, born in Assuit-Egypt, on November 18, 1990. He received his B.Sc. degree from El-Minia High Institute for Engineering and Technology, Department of Electrical Engineering, El-Minia, Egypt since 2012. The.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book introduces planning and evaluation of grid-connected photovoltaic (PV) systems-based microgrid to supply Assiut University main campus with electricity. The microgrid system requires a planning policy that can anticipate how much electricity will be consumed to meet future power demands. So, the work of this book starts with the electrical energy consumption forecasting. The forecasting process, in this book, adopts two machine learning tools that are Gaussian process (GP) tool and neural networks technique. The forecasting methodology is divided into two sub-models. The first one is a neural network model in the context of nonlinear autoregressive (NAR) model that can predict future values of a set of exogenous variables affecting electrical energy consumption. The second one is a GP model, which can be trained for relating the predicted exogenous variables to the electrical energy consumption in the process of future electrical energy consumption forecasting. In this book, the GP approach has demonstrated reasonable forecasting for one year ahead with a mean absolute percentage error (MAPE) of 4.9 %.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 192 pp. Englisch.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book introduces planning and evaluation of grid-connected photovoltaic (PV) systems-based microgrid to supply Assiut University main campus with electricity. The microgrid system requires a planning policy that can anticipate how much electricity will be consumed to meet future power demands. So, the work of this book starts with the electrical energy consumption forecasting. The forecasting process, in this book, adopts two machine learning tools that are Gaussian process (GP) tool and neural networks technique. The forecasting methodology is divided into two sub-models. The first one is a neural network model in the context of nonlinear autoregressive (NAR) model that can predict future values of a set of exogenous variables affecting electrical energy consumption. The second one is a GP model, which can be trained for relating the predicted exogenous variables to the electrical energy consumption in the process of future electrical energy consumption forecasting. In this book, the GP approach has demonstrated reasonable forecasting for one year ahead with a mean absolute percentage error (MAPE) of 4.9 %.