Deep Concatenated Residual Network With Bidirectional LSTM for One-Hour-Ahead Wind Power Forecasting

Ko, Min-Seung; Lee, Kwangsuk; Kim, Jae-Kyeong; Hong, Chae-Seon; Zhang, Dong; Hur, Kyeong

doi:10.1109/tste.2020.3043884

Cited by 167 publications

(61 citation statements)

References 74 publications

(79 reference statements)

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“…Wind energy is another typical renewable energy resource which has a higher volatility in general. Motivated by reducing the possibility of the overfitting of the prevailing LSTM model, a concatenated residual learning with stacked bidirectional long short-term memory (Bi-LSTM) layers by connecting the multi-level residual network and DenseNet is proposed for wind energy forecasting in [42]. In [45], a hierarchical forecasting is introduced for wind power energy where a generalized least squares method is firstly established for reconciling wind power prediction at different levels to achieve better accuracy [45].…”

Section: A Forecasting Tasksmentioning

confidence: 99%

AI-powered Energy Internet Towards Carbon Neutrality: Challenges and Opportunities

Li¹

2021

Preprint

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From self-driving vehicles, voice recognition based virtual digital assistants, smart thermostats to recommendation systems, Artificial Intelligence (AI) is becoming a crucial part of the carbon neutral society that has drawn considerable interest from energy supply firms, startups, technology developers, financial institutions, national governments and the academic community. The emergence of AI initiates numerous opportunities to transform energy industry to AI-powered smart system which can revolutionize traditional approaches of creativity thinking, strategical operation, and solution seeking, especially for accelerating carbon neutrality of our society. This survey provides a comprehensive overview of fundamental principles that underpin applications of big data analysis in Energy Internet (EI), such as smart energy supply and consumption, smart health and Fintech. Next, we focus on intelligent decision-making for the energy industry and inform the state-of-the-art by thoroughly reviewing the literature. Subsequently, cybersecurity issues for AI system related to EI are discussed with recent advancements from vulnerability analysis of AI system to differential privacy and to blockchain based security technology. To our knowledge, this is one of the first academic, peer-reviewed works to provide a systematic review of AI applications for EI research and initiatives in terms of big data analysis, intelligent decision-making and AI related cybersecurity These initiatives were systematically classified into different groups according to the field of application, methodology and contribution Afterwards, potential challenges, limitations for existing research and opportunities for future directions are discussed, ranging from emerging explainable AI, to localized multi-energy marketplaces, self-driving electric vehicle charging and e-mobility. This paper can help us understand how to build smart cities and critical infrastructure for a climate-changed world towards the UN’s sustainable development goals.

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Section: A Forecasting Tasksmentioning

confidence: 99%

AI-powered Energy Internet Towards Carbon Neutrality: Challenges and Opportunities

Li¹

2021

Preprint

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“…It has been considered trivial because of the volatile nature of energy consumption at the customer end. Hence, the SPV energy load prediction and recommender system for the same remain open [20] [21].…”

Section: Introductionmentioning

confidence: 99%

AI-Empowered Recommender System for Renewable Energy Harvesting in Smart Grid System

Patel¹,

Kumari

Tanwar

et al. 2022

IEEE Access

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The electric grid has already been transitioned towards a more flexible, intelligent, and interactive grid system, i.e., Smart Grid (SG) for load management, energy prediction, higher penetration of renewable energy generation, future planning, and operations. However, there is a huge gap between energy demand and supply due to the rise of different electric products and electric vehicles. Renewable Energy Harvesting (REH) plays a critical role in managing this demand response gap, where energy is generated from various renewable energy resources such as Solar PhotoVoltaic (SPV) and wind energy. Several research works exist in this regard. However, they have not yet been exploited fully. So, this paper proposed AI-RSREH approach, i.e., the AI-empowered Recommender System for REH in residential houses. The main goal of the proposed AI-RSREH approach is to predict energy generation based on SPV accurately, and this study aims to minimize the gap between the actual generation of energy and the predicted energy generation along with a recommender system for SPV installation. An exploratory residential house-wise data analytics is conducted for the demand response gap. AI-RSREH uses a stacked Long-Short Term Memory (LSTM) model to predict energy generation with a recommender system based on the energy generation prediction result. The effectiveness of the proposed approach is evaluated based on the SPV installation in residential houses and prediction accuracy compared to the existing methods.

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“…The architecture of these networks overcame the weaknesses of traditional RNNs in capturing long-term dependencies, as shown by Bengiot et al [18]. With this feature, these networks have been widely used to solve time series forecasting problems [11,[19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Tourism Demand Forecasting Based on an LSTM Network and Its Variants

Hsieh

2021

Algorithms

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The need for accurate tourism demand forecasting is widely recognized. The unreliability of traditional methods makes tourism demand forecasting still challenging. Using deep learning approaches, this study aims to adapt Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit networks (GRU), which are straightforward and efficient, to improve Taiwan’s tourism demand forecasting. The networks are able to seize the dependence of visitor arrival time series data. The Adam optimization algorithm with adaptive learning rate is used to optimize the basic setup of the models. The results show that the proposed models outperform previous studies undertaken during the Severe Acute Respiratory Syndrome (SARS) events of 2002–2003. This article also examines the effects of the current COVID-19 outbreak to tourist arrivals to Taiwan. The results show that the use of the LSTM network and its variants can perform satisfactorily for tourism demand forecasting.

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Deep Concatenated Residual Network With Bidirectional LSTM for One-Hour-Ahead Wind Power Forecasting

Cited by 167 publications

References 74 publications

AI-powered Energy Internet Towards Carbon Neutrality: Challenges and Opportunities

AI-powered Energy Internet Towards Carbon Neutrality: Challenges and Opportunities

AI-Empowered Recommender System for Renewable Energy Harvesting in Smart Grid System

Tourism Demand Forecasting Based on an LSTM Network and Its Variants

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