Application of artificial neural networks to unit commitment

Sendaula, M.; Biswas, Saroj; Eltom, Ahmed; Parten, C.; Kazibwe, W.E.

doi:10.1109/ann.1991.213467

Cited by 15 publications

(4 citation statements)

References 8 publications

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“…The current methods on UC can be classified into two main clusters: 1) direct methods that give direct solutions with resolving techniques; 2) heuristic methods that employ artificial intelligence or heuristic natural rules. Integer/mixed-integer programming [30,31] Dynamic and linear programming [32]- [36] Branch-and-bound method [37,38] Lagrangian relaxation [39] Decomposition techniques [40]- [43] Heuristic methods Priority list [44]- [46] Expert system [47]- [49] Artificial neural networks [50]- [52] Fuzzy logic approach [53,54] Genetic algorithm [55,56] Evolutionary programming [57]- [59] Simulated annealing algorithm [60]- [62] Ant colony algorithm [63] Particle swarm optimization [64] Binary fish swarm [65] Tabu search [66,67] Hybrid techniques [68]- [72] Some representative works are listed in Table I. Since the main concern of this paper is to review stability related UC problems, the details of these methods for traditional UC are not discussed here.…”

Section: B State-of-art Of Generation Scheduling Methodsmentioning

confidence: 99%

Stability-Constrained Power System Scheduling: A Review

Luo

Teng

2020

IEEE Access

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Section: B State-of-art Of Generation Scheduling Methodsmentioning

confidence: 99%

Stability-Constrained Power System Scheduling: A Review

Luo

Teng

2020

IEEE Access

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“…• Simulated Annealing: bUC in Zhuang and Galiana (1990); Annakkage et al (1995); Simopoulos et al (February 2006); Mantawy et al (1998), Ramp Rate in Simopoulos et al (February 2006), Crew in Zhuang and Galiana (1990); Annakkage et al (1995); Mantawy et al (1998), and Maintenance in Zhuang and Galiana (1990); (2007); Wang and Shahidehpour (1993); Dieu and Ongsakul (2008); Nayak and Sharma (2000); Liang and Kang (2000); Luh et al (1999), Ramp Rate in Sendaula et al (1991); Abdelaziz et al (2010); Wang and Shahidehpour (1993); Dieu and Ongsakul (2008), Crew in Nayak and Sharma (2000), and Hydro-Thermal in Walsh and O'Malley (1997); • Genetic Algorithm: bUC in Wong (1996, 1994) and Multi-Area in Chandrasekaran and Simon (2012b).…”

Section: Guided Random Explorationmentioning

confidence: 99%

Large-scale Unit Commitment under uncertainty

Ackooij²,

et al. 2015

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The Unit Commitment problem in energy management aims at finding the optimal productions schedule of a set of generation units while meeting various system-wide constraints. It has always been a large-scale, non-convex difficult problem, especially in view of the fact that operational requirements imply that it has to be solved in an unreasonably small time for its size. Recently, the ever increasing capacity for renewable generation has strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex, uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focusing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, also providing entry points to the relevant literature on optimization under uncertainty

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“…-recognition of printed (or handwritten) characters and text (Chandra and Sudhakar 1988, Jackel et al 1988, LeCun et al 1989, Zurada et al 1991, -medical image analysis (Dayhoff and Dayhoff 1988), -recognition of speech (Prager et al 1989, Doutriaux and Zipser 1991, Laboissiere et al 1991 -segmentation and classification of regions from images (Cottrell1991), -machine vision (Koch 1987, Booth et al 1989, and -signal processing (Damarla et al 1991, Chakrabarti and Bindal 1991, Shahani et al 1991 180…”

Section: Patten Recognition and Classificationmentioning

confidence: 98%

Neural networks in manufacturing: A survey

Huang

Zhang

Proceedings of 15th IEEE/CHMT International Electronic Manufacturing Technology Symposium

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Artificial Intelhgence (AI) has been claimed to yield revolutionary advances in manufacturing. AI technology is applicable to the entire range of manufacturing activities. As an AI technique, expert systems have been wildly used in manufacturing for over two decades. Recently, another AI technique, namely, neural networks, are gaining more and more visibility and have been successfully applied in manufacturing practices. While most of the survey papers about AI in manufacturing have been focused on expert systems application, fewer attentions have been paid to neural networks. However, expert systems are less effective in the ever-changing , complex, and open system environment of today's manufacturing systems. On the other hand, neural networks are able to learn, adapt to changes, and can mimic human thought processes with little human interventions. They could be of great help for the present Computer Integrated Manufacturing (CIM) and the future Intelligent Manufacturing Systems (IMS). Therefore, it is necessary to present the state-of-the-art of neural networks in manufacturing. The objective of this paper is to update information about neural networks in manufacturing, which will provide some guidelines and references 'for the research and implementation. In this paper, the basic concepts of neural networks are briefly introduced, Then, a survey of neural network applications in manufacturing is provided. Finally, the projection of future trends is given to help make decisions concerning neural networks implementation in manufacturing today and to aid in guiding research for tomorrow.

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Application of artificial neural networks to unit commitment

Cited by 15 publications

References 8 publications

Stability-Constrained Power System Scheduling: A Review

Stability-Constrained Power System Scheduling: A Review

Large-scale Unit Commitment under uncertainty

Neural networks in manufacturing: A survey

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