Midterm Power Load Forecasting Model Based on Kernel Principal Component Analysis and Back Propagation Neural Network with Particle Swarm Optimization

Zhao, Libin; Sun, Xiuyun; Wang, Shuai; Pan, Mengjiao; Zhang, Yue; Ji, Zhendong

doi:10.1089/big.2018.0118

Cited by 35 publications

(13 citation statements)

References 14 publications

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“…[122] implemented a DNN paradigm with an enhanced training algorithm that consists of two search algorithms for MTLF in the power grid and shows the efficacy of the method. In [123] an integrated neural network-based model with particle swarm optimization (PSO) technique was proposed. The model demonstrated viability and strength over other models.…”

Section: B: Meduim-term Load Forecastingmentioning

confidence: 99%

Integrating Artificial Intelligence Internet of Things and 5G for Next-Generation Smartgrid: A Survey of Trends Challenges and Prospect

2022

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Smartgrid is a paradigm that was introduced into the conventional electricity network to enhance the way generation, transmission, and distribution networks interrelate. It involves the use of Information and Communication Technology (ICT) and other solution in fault and intrusion detection, mere monitoring of energy generation, transmission, and distribution. However, on one hand, the actual and earlier smartgrid, do not integrate more advanced features such as automatic decision making, security, scalability, self-healing and awareness, real-time monitoring, cross-layer compatibility, etc. On the other hand, the emergence of the digitalization of the communication infrastructure to support the economic sector which among them are energy generation and distribution grid with Artificial Intelligence (AI) and largescale Machine to Machine (M2M) communication. With the future Massive Internet of Things (MIoT) as one of the pillars of 5G/6G network factory, it is the enabler to support the next generation smart grid by providing the needed platform that integrates, in addition to the communication infrastructure, the AI and IoT support, providing a multitenant system. This paper aim at presenting a comprehensive review of next smart grid research trends and technological background, discuss a futuristic next-generation smart grid driven by artificial intelligence (AI) and leverage by IoT and 5G. In addition, it discusses the challenges of nextgeneration smart-grids as it relate to the integration of AI, IoT and 5G for better smart grid architecture. Also, proffers possible solutions to some of the challenges and standards to support this novel trend. A corresponding future work will dwell on the implementation of the discussed integration of AI, IoT and 5G for next-generation smart grid, using Matlab, NS2/NS3, Open-daylight and Mininet as soft tools and compare with related literature.

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Section: B: Meduim-term Load Forecastingmentioning

confidence: 99%

Integrating Artificial Intelligence Internet of Things and 5G for Next-Generation Smartgrid: A Survey of Trends Challenges and Prospect

2022

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“…In Askari and Keynia [86], the authors deployed a DNN model with an optimized training algorithm that comprises two search algorithms for MTLF in power systems and presented the effectiveness of the model. Liu et al [87] also provided a neural network-based model with particle swarm optimization (PSO) and showed the feasibility and validity of the model. Rai and De [88] improved a support vector regression model for MTLF with an average minimum mean absolute percentage error (MAPE) of 3.60.…”

Section: Mid-term Load Forecastingmentioning

confidence: 99%

Artificial Intelligence Techniques in Smart Grid: A Survey

2021

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The smart grid is enabling the collection of massive amounts of high-dimensional and multi-type data about the electric power grid operations, by integrating advanced metering infrastructure, control technologies, and communication technologies. However, the traditional modeling, optimization, and control technologies have many limitations in processing the data; thus, the applications of artificial intelligence (AI) techniques in the smart grid are becoming more apparent. This survey presents a structured review of the existing research into some common AI techniques applied to load forecasting, power grid stability assessment, faults detection, and security problems in the smart grid and power systems. It also provides further research challenges for applying AI technologies to realize truly smart grid systems. Finally, this survey presents opportunities of applying AI to smart grid problems. The paper concludes that the applications of AI techniques can enhance and improve the reliability and resilience of smart grid systems.

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“…Multiagents systems have also been applied to optimize deep learning models. The use of particle swarm optimization (PSO) can be found in Liu et al 16 The authors proposed a model based on kernel principal component analysis and back propagation neural network with PSO for midterm power load forecasting. The hybridization of deep learning models with PSO was also explored in Fernandes-Junior and Yen 17 but, this time, the authors applied the methodology with image classification purposes.…”

Section: Related Workmentioning

confidence: 99%

Coronavirus Optimization Algorithm: A Bioinspired Metaheuristic Based on the COVID-19 Propagation Model

et al. 2020

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This study proposes a novel bioinspired metaheuristic simulating how the coronavirus spreads and infects healthy people. From a primary infected individual (patient zero), the coronavirus rapidly infects new victims, creating large populations of infected people who will either die or spread infection. Relevant terms such as reinfection probability, super-spreading rate, social distancing measures, or traveling rate are introduced into the model to simulate the coronavirus activity as accurately as possible. The infected population initially grows exponentially over time, but taking into consideration social isolation measures, the mortality rate, and number of recoveries, the infected population gradually decreases. The coronavirus optimization algorithm has two major advantages when compared with other similar strategies. First, the input parameters are already set according to the disease statistics, preventing researchers from initializing them with arbitrary values. Second, the approach has the ability to end after several iterations, without setting this value either. Furthermore, a parallel multivirus version is proposed, where several coronavirus strains evolve over time and explore wider search space areas in less iterations. Finally, the metaheuristic has been combined with deep learning models, to find optimal hyperparameters during the training phase. As application case, the problem of electricity load time series forecasting has been addressed, showing quite remarkable performance.

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Midterm Power Load Forecasting Model Based on Kernel Principal Component Analysis and Back Propagation Neural Network with Particle Swarm Optimization

Cited by 35 publications

References 14 publications

Integrating Artificial Intelligence Internet of Things and 5G for Next-Generation Smartgrid: A Survey of Trends Challenges and Prospect

Integrating Artificial Intelligence Internet of Things and 5G for Next-Generation Smartgrid: A Survey of Trends Challenges and Prospect

Artificial Intelligence Techniques in Smart Grid: A Survey

Coronavirus Optimization Algorithm: A Bioinspired Metaheuristic Based on the COVID-19 Propagation Model

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