Intelligent data-driven modelling and optimization in power and energy applications /
Intelligent data-driven modelling and optimization in power and energy applications /
edited by B Rajanarayan Prusty, Neeraj Gupta, Kishore Bingi, and Rakesh Sehgal.
- 1st Edition.
- 238 pages: illustrations; 16 cm.
- Intelligent Data-Driven Systems and Artificial Intelligence .
Includes index.
Chapter 1 - Preprocessing approaches for data-driven modellingKishore Bingi, B. Rajanarayan Prusty, and Neeraj GuptaChapter 2 - Power system planning using data-driven modelsB. Rajanarayan Prusty, Sujith Jacob, and Kishore BingiChapter 3 - Data-driven analytics for power system stability assessmentPurna Prakash Kasaraneni, Yellapragada Venkata Pavan Kumar, and Ramani KannanChapter 4 - Data-driven machine learning models for load power forecasting in photovoltaic systemsPrem Prakash Vuppuluri, K. Pritam Satsangi, Pihu Agarwal, and Tania AroraChapter 5 - Forecasting of renewable energy using fractional-order neural networksBhukya Ramadevi, Venkata Ramana Kasi, Kishore Bingi, B. Rajanarayan Prusty, and Madiah OmarChapter 6 - Data-driven photovoltaic system characteristic determination using non-linear system identificationYellapragada Venkata Pavan Kumar, Challa Pradeep Reddy, Ramani Kannan, and Purna Prakash KasaraneniChapter 7 - Fractional feed-forward neural network-based smart grid stability prediction modelBhukya Ramadevi, Venkata Ramana Kasi, Kishore Bingi, Rosdiazli Ibrahim, and B. Rajanarayan PrustyChapter 8 - Data-driven optimization framework for microgrid energy managementMohamed Atef, Moslem Uddin, Md Masud Rana, Md Rasel Sarkar, and G.M. ShafiullahChapter 9 - Optimization of controllers for sustained buildingGaurav KumarChapter 10 - Intelligent data-driven approach for fractional-order wireless power transfer systemArshaque Ali, Ashneel Kumar, Utkal Mehta, and Maurizio Cirrincione
Electronic reproduction.
Ipswich, MA
Available via World Wide Web.
9781003470274 1003470270 9781040016251 1040016251 1040016111 9781040016114 1003470270 1040016251 1040016111 1032707909 1032472065
9781003470274 10.1201/9781003470274 doi
9781003470274 Taylor & Francis
Renewable energy sources--Computer simulation.
Artificial intelligence--Engineering applications.
TJ808 / .I57 2024eb
621.042 INT
Includes index.
Chapter 1 - Preprocessing approaches for data-driven modellingKishore Bingi, B. Rajanarayan Prusty, and Neeraj GuptaChapter 2 - Power system planning using data-driven modelsB. Rajanarayan Prusty, Sujith Jacob, and Kishore BingiChapter 3 - Data-driven analytics for power system stability assessmentPurna Prakash Kasaraneni, Yellapragada Venkata Pavan Kumar, and Ramani KannanChapter 4 - Data-driven machine learning models for load power forecasting in photovoltaic systemsPrem Prakash Vuppuluri, K. Pritam Satsangi, Pihu Agarwal, and Tania AroraChapter 5 - Forecasting of renewable energy using fractional-order neural networksBhukya Ramadevi, Venkata Ramana Kasi, Kishore Bingi, B. Rajanarayan Prusty, and Madiah OmarChapter 6 - Data-driven photovoltaic system characteristic determination using non-linear system identificationYellapragada Venkata Pavan Kumar, Challa Pradeep Reddy, Ramani Kannan, and Purna Prakash KasaraneniChapter 7 - Fractional feed-forward neural network-based smart grid stability prediction modelBhukya Ramadevi, Venkata Ramana Kasi, Kishore Bingi, Rosdiazli Ibrahim, and B. Rajanarayan PrustyChapter 8 - Data-driven optimization framework for microgrid energy managementMohamed Atef, Moslem Uddin, Md Masud Rana, Md Rasel Sarkar, and G.M. ShafiullahChapter 9 - Optimization of controllers for sustained buildingGaurav KumarChapter 10 - Intelligent data-driven approach for fractional-order wireless power transfer systemArshaque Ali, Ashneel Kumar, Utkal Mehta, and Maurizio Cirrincione
Electronic reproduction.
Ipswich, MA
Available via World Wide Web.
9781003470274 1003470270 9781040016251 1040016251 1040016111 9781040016114 1003470270 1040016251 1040016111 1032707909 1032472065
9781003470274 10.1201/9781003470274 doi
9781003470274 Taylor & Francis
Renewable energy sources--Computer simulation.
Artificial intelligence--Engineering applications.
TJ808 / .I57 2024eb
621.042 INT