Ground-Based Measurement for Solar Power Variability Forecasting Modeling Using Generalized Neural Network
01 Jan 2015-Vol. 327, pp 49-61
TL;DR: Ground-based measurements of solar photovoltaic power are used for the forecasting of 43-kW A-Si SPV system at IIT, Jodhpur and generalized neural network is used for forecasting the PV power variability.
Abstract: The primary aim of this paper is to analyze solar power variability Ground-based measurements of solar photovoltaic power are used for the forecasting of 43-kW A-Si SPV system In this study, we describe the variability in the power production of solar photovoltaic plant at IIT, Jodhpur Solar PV generation forecasting is playing a key role in accurate solar power dispatchability as well as scheduling of PV power for hybrid power generation systems The actual power produced by a PV power system varies according to variation in meteorological parameters and efficiency of PV system components For the purpose of forecasting as per the schedule in the Indian power sector, a time slot of 15 min is considered for each forecasting The proposed generalized neural network technique will be appropriated for modeling of solar power variability forecasting In this paper, we used generalized neural network for forecasting the PV power variability
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01 Dec 2015
TL;DR: In this article, a two stage procedure is used referred to as GNN (Generalized Neural Network) model for forecasting the power generated in a 5MW solar PV plant owned by Gujarat Power Corporation Limited (GPCL) at Charanka solar park, Gujarat.
Abstract: . The percentage of renewable energy sources such as solar, wind power and biomass in the energy mix of India is increasing every year. Solar power variability is an important issue for grid integration of solar photovoltaic power plants. The main objective of this paper is to forecast the power generated in a 5 MW solar PV plant owned by Gujarat Power Corporation Limited (GPCL) at Charanka solar park, Gujarat. Charanka is a location with an average of320 sunny days in a year. Average solar insolation available here is 5.7–6.0 kWh/m2 per day. Data obtained from 1st March 2014–31st August 2014 is used for analysis purposes. In this paper a two stage procedure is used referred to as GNN (Generalized Neural Network) model. In the primary stage pre-processing is done on the raw data followed by neural network model for forecasting.
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Book•
01 Jul 1994TL;DR: In this chapter seven Neural Nets based on Competition, Adaptive Resonance Theory, and Backpropagation Neural Net are studied.
Abstract: 1. Introduction. 2. Simple Neural Nets for Pattern Classification. 3. Pattern Association. 4. Neural Networks Based on Competition. 5. Adaptive Resonance Theory. 6. Backpropagation Neural Net. 7. A Sampler of Other Neural Nets. Glossary. References. Index.
2,665 citations
TL;DR: In this paper, the authors focus on the implementation and development of ICT in the education sector, challenging and developing the traditional learning environment whilst introducing new educational tools including e-learning.
Abstract: In this article, we focus on the implementation and development of ICT in the education sector, challenging and developing the traditional learning environment whilst introducing new educational tools including e-learning. The paper investigates ICT as a tool empowering and developing learners lifelong learning opportunities. It defines a model of ICT development and identifies three core development stages through which basic skills, ICT skills, and lifelong learning skills are acquired. The paper further gives a description of the ICT impact on labour and education markets, the current state of development of ICT at school in EU and the needs for further investment in this area. The findings of this paper suggest that such investment is likely to have positive effects when geared towards blended learning approaches built on comprehensive policy ...
617 citations
TL;DR: This paper presents meta-learning evolutionary artificial neural network (MLEANN), an automatic computational framework for the adaptive optimization of artificial neural networks (ANNs) wherein the neural network architecture, activation function, connection weights; learning algorithm and its parameters are adapted according to the problem.
159 citations
TL;DR: In this article, the authors apply energy analysis and economic analysis in order to assess the application of solar photovoltaics (PVs) in buildings and make a comparison both to electricity supply from centralised PV plants and to conventional electricity sources.
140 citations
Book•
20 Aug 2008
TL;DR: This book is an introduction to some new fields in soft computing with its principal components of fuzzy logic, ANN and EA and it is hoped that it would be quite useful to study the fundamental concepts on these topics for the pursuit of allied research.
Abstract: Intuitive consciousness/ wisdom is also one of the frontline areas in soft computing, which has to be always cultivated by meditation. This book is an introduction to some new fields in soft computing with its principal components of fuzzy logic, ANN and EA and it is hoped that it would be quite useful to study the fundamental concepts on these topics for the pursuit of allied research. The approach in this book is to provides an understanding of the soft computing field, to work through soft computing using examples, to integrate pseudo - code operational summaries and Matlab codes, to present computer simulation, to include real world applications and to highlight the distinctive work of human consciousness in machine. "I believe the chapters would help in understanding not only the basic issues and characteristic features of soft computing, but also the aforesaid problems of CTP and in formulating possible solutions. Dr. Chaturvedi deserves congratulations for bringing out the nice piece of work." Sankar K. Pal, Director Indian Statistical Institute
130 citations