data communication analytics smart von ahmed arslan (8 Ergebnisse)

Zustand: New.

Taschenbuch. Zustand: Neu. Neuware -Energy utilities are constantly under pressure to meet the growing complicated energy demands. The traditional energy grid allows for one-way communication of energy usage between customers and utilities. This does not allow utilities to control or to suggest any changes in the consumption based on the obtained energy data. In this book, we design and implement innovative secure and reliable two-way communication between homes and the Utility. In this context, different houses communicate their energy usage, while an electric transformer relays action requests from the energy utility's headquarters. This enables the real-time tracking of energy usage by both consumers and the utility. Therefore, the efficiency of energy generation and distribution is enhanced, and consumers are empowered to make smarter decisions about their consumption. To this end, we develop and compare several machine Learning and Data Analytics models predicting energy consumption. The obtained results show that our proposed models perform better than existing ones for time-series energy forecasting.Books on Demand GmbH, Überseering 33, 22297 Hamburg 136 pp. Englisch.

Taschenbuch. Zustand: Neu. Data Communication and Analytics for Smart Grid Systems | Diverse Forecasting Models | Arslan Ahmed (u. a.) | Taschenbuch | Englisch | 2020 | LAP LAMBERT Academic Publishing | EAN 9786202924269 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Energy utilities are constantly under pressure to meet the growing complicated energy demands. The traditional energy grid allows for one-way communication of energy usage between customers and utilities. This does not allow utilities to control or to suggest any changes in the consumption based on the obtained energy data. In this book, we design and implement innovative secure and reliable two-way communication between homes and the Utility. In this context, different houses communicate their energy usage, while an electric transformer relays action requests from the energy utility's headquarters. This enables the real-time tracking of energy usage by both consumers and the utility. Therefore, the efficiency of energy generation and distribution is enhanced, and consumers are empowered to make smarter decisions about their consumption. To this end, we develop and compare several machine Learning and Data Analytics models predicting energy consumption. The obtained results show that our proposed models perform better than existing ones for time-series energy forecasting. 136 pp. Englisch.

Zustand: New. Print on Demand.

Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ahmed ArslanArslan Ahmed received his Master of Applied Science degree in Electrical Engineering from Carleton University, Ottawa, Canada. He is now a Data Scientist at IBM, Toronto, Canada. Dr. Zied Bouida and Professor Mohamed Ibnk.

Zustand: New. PRINT ON DEMAND.

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Energy utilities are constantly under pressure to meet the growing complicated energy demands. The traditional energy grid allows for one-way communication of energy usage between customers and utilities. This does not allow utilities to control or to suggest any changes in the consumption based on the obtained energy data. In this book, we design and implement innovative secure and reliable two-way communication between homes and the Utility. In this context, different houses communicate their energy usage, while an electric transformer relays action requests from the energy utility's headquarters. This enables the real-time tracking of energy usage by both consumers and the utility. Therefore, the efficiency of energy generation and distribution is enhanced, and consumers are empowered to make smarter decisions about their consumption. To this end, we develop and compare several machine Learning and Data Analytics models predicting energy consumption. The obtained results show that our proposed models perform better than existing ones for time-series energy forecasting.