Deep learning for energy markets

Polson, Michael; Sokolov, Vadim

doi:10.1002/asmb.2518

Cited by 16 publications

(13 citation statements)

References 47 publications

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“…Figure 7 shows the layers of the LSTM. The hidden layer of the LSTM is called a memory cell which is the fundamental part of the LSTM [36]. There are three gates: input gates, output gates, and forget gates.…”

Section: Methodsmentioning

confidence: 99%

A Fault Diagnosis Design Based on Deep Learning Approach for Electric Vehicle Applications

2021

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Diagnosing faults in electric vehicles (EVs) is a great challenge. The purpose of this paper is to demonstrate the detection of faults in an electromechanical conversion chain for conventional or autonomous EVs. The information and data coming from different sensors make it possible for EVs to recover a series of information including currents, voltages, speeds, and so on. This information is processed to detect any faults in the electromechanical conversion chain. The novelty of this study is to develop an architecture for a fault diagnosis model by means of the feature extraction technique. In this regard, the long short-term memory (LSTM) approach for the fault diagnosis is proposed. This approach has been tested for an EV prototype in practice, is superior in accuracy over other fault diagnosis techniques, and is based on machine learning. An EV in an urban context is modeled, and then the fault diagnosis approach is applied based on deep learning architectures. The EV and the fault diagnosis model is simulated in Matlab software. It is also revealed how deep learning contributes to the fault diagnosis of EVs. The simulation and practical results confirm that higher accuracy in the fault diagnosis is obtained by applying the LSTM.

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Section: Methodsmentioning

confidence: 99%

A Fault Diagnosis Design Based on Deep Learning Approach for Electric Vehicle Applications

2021

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“…Dissertation implements DL with extreme value theory (EVT) to predict peak load in the main grid to improvise prediction accuracy. 58 Paper 59 analysis generating power and energy consumption together with weather prediction as numerical simulation-based analysis. It is evaluated by testing a small-scale building around the university campus exhibits result feasibly with reliability.…”

Section: Demand Forecastingmentioning

confidence: 99%

“…Energy consumption is made influenced by environmental dataset like weather, temperature, and atmospheric pressure. Dissertation implements DL with extreme value theory (EVT) to predict peak load in the main grid to improvise prediction accuracy 58 . Paper 59 analysis generating power and energy consumption together with weather prediction as numerical simulation‐based analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep learning and reinforcement learning approach on microgrid

Kumar¹,

Kandasamy

Ramanathan

2020

Int Trans Electr Energ Syst

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Microgrid is a new era in the power system and it has more scope of investigation on research. Due to an increase in demand and future expansion of the power system, analyzing the complexities of the network becomes a challenging task. Artificial intelligence plays a vital role in resolving such issues in a microgrid in various aspects. Owing to the rapid growth of periodical update in computational cost reduction, enhanced data analysis-based algorithm artificial intelligence enters into new epoch Artificial Intelligence AI 2.0. Based on such approach, machine learning has been evolved as AI 2.0 initially. Now, it develops branches like deep learning, reinforcement learning, and a combination of both deep reinforcement learning algorithms. These algorithms are precise to attain higher priority in decision-making under a complex network. This paper deals with numerous challenges of the above algorithm to state the significance of AI 2.0 and summarization of their application toward microgrid is useful to analyze the power system. K E Y W O R D S deep learning, deep reinforcement learning, microgrid, reinforcement learning 1 | INTRODUCTION Microgrid is a cluster of distributed generators (DG), stored energy system, local loads along with protecting and monitoring devices. Microgrid has its own specialty in operating under two modes of operation: autonomous and grid-connected mode, mode selection attained by the use of static transfer switch that bridges operation selection in microgrid. The presence of multiple DGs creates a distribution access problem in multipoint is transformed into a concentrated

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“…However, compared with the traditional electricity charge settlement, the market‐based settlement after the electric structural reform is much more complicated 2 . The market members have increased, the variety of transactions has been diversified, and the rules of the trading contracts have become more flexible.…”

Section: Introductionmentioning

confidence: 99%

Smart contract for electricity transactions and charge settlements using blockchain

Wu²,

Cheng³

et al. 2020

Appl Stoch Models Bus & Ind

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In this article, aimed at the future “let go” electricity market, smart contracts for grid enterprises doing electricity transactions and charge settlements based on blockchain technology, as well as the trading model using the smart contracts, are proposed. Then the key technological difficulties are analyzed, and the solutions are given. The main goal of our research is to help developing the infrastructure for electricity market members, and match their bilateral trading. By running a smart contract instance in a peer‐to‐peer network composed by 4000 nodes, experiments show that the success rate is 99.38% and the average time consumption for each transaction is 16 seconds. If our method is applied, we can reduce the trust cost of the electric electricity market, and improve the efficiency of the electricity transaction and charge settlement.

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Deep learning for energy markets

Cited by 16 publications

References 47 publications

A Fault Diagnosis Design Based on Deep Learning Approach for Electric Vehicle Applications

A Fault Diagnosis Design Based on Deep Learning Approach for Electric Vehicle Applications

Deep learning and reinforcement learning approach on microgrid

Smart contract for electricity transactions and charge settlements using blockchain

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