A Novel Reinforced Deep RNN–LSTM Algorithm: Energy Management Forecasting Case Study

Xia, Fang; Zhang, Wei; Guo, Yuhao; Wang, Jie; Wang, Mei; Li, Shunlei

doi:10.1109/tii.2021.3136562

Cited by 23 publications

(5 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The original hybrid deep learning algorithm in the work [33] was developed to make a computer-assisted forecasting energy management system, and a Hankel matrix is created for processing gathered automatic metering infrastructure load information by applying the Copula function. A robust energy management system in work [34] with an inconsistent energy supply aiming to minimize energy costs while avoiding failing to satisfy energy demands was proposed through an algorithm based on safe reinforcement learning, which can effectively exploit short-horizon forecasts on system uncertainties.…”

Section: B Smart Energy Forecasting Systemsmentioning

confidence: 99%

Robust Vehicle-to-Grid Energy Trading Method Based on Smart Forecast and Multi-Blockchain Network

Liang,

Wang,

Abdallah

2024

IEEE Access

View full text Add to dashboard Cite

In the present era, energy issues are a significant concern, and the energy trading market is the crucial sector to facilitate supply-demand balance and sustainable development. For better demand response and grid balancing, vehicle-to-grid (V2G) technology is rapidly gaining importance in energy markets. To narrow the gap between ideal V2G goals and actual applications needs, energy trading system has to overcome the challenges of over-centralized structure, inflexible timeline adaptation, limited market scale and energy efficiency, excessive feedback time costs, and low rate of economic return. To address these issues and ensure a secure energy market, we propose a decentralized intelligent V2G system called V2G Forecasting and Trading Network (V2GFTN) to achieve efficient and robust energy trading in campus EV networks. A multiple blockchain structure is proposed in V2GFTN to ensure trading security and data privacy between energy requests and offers. V2GFTN also integrates energy forecasting functions for EVs with a smart energy trading and EV allocation mechanism called SRET so that the EVs with driving tasks can supply their extra power back to the grid and achieve higher energy efficiency and economic profit. Through rigorous experimentation and compared with equivalent studies, V2GFTN system has demonstrated higher economic profit and energy demand fill rate by up to 1.6 times and 1.9 times than the state-of-the-art V2G approaches.

show abstract

Section: B Smart Energy Forecasting Systemsmentioning

confidence: 99%

Robust Vehicle-to-Grid Energy Trading Method Based on Smart Forecast and Multi-Blockchain Network

Liang,

Wang,

Abdallah

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…According to that study, decimal methods had the shortest run time of 32 seconds, but the softmax normalization method gave the lowest RMSE. Copula notion is another method adopted [34], where data normalization is achieved by using Copula, a pre-processing technique that enhances peak load forecasting precision. Calendar features are mostly categorical variables and not continuous in nature.…”

Section: B: Techniques To Transform the Datamentioning

confidence: 99%

Energy Demand Forecasting and Optimizing Electric Systems for Developing Countries

et al. 2023

View full text Add to dashboard Cite

under Grant 56.00.0000. 028.20.0005.19-440; and in part by the Innovation Design and Entrepreneurship Academy, (iDEA) project of Bangladesh Computer Council (BCC), Bangladesh under Grant Mujib1002021/680. We thank our colleagues from Advanced Intelligent Multidisciplinary Systems (AIMS) Laboratory, United International University (UIU), Bangladesh who provided insights and expertise that greatly assisted the research.

show abstract

“…Next, study in [11] is a paper related to RNN whereby authors developed a forecasting energy management based on "pooling" RNN. An additional pooling-based deep neural network module coupled with mathematical function (Copula) are added into the existing architecture.…”

Section: Related Workmentioning

confidence: 99%

“…The hyperparameters are; b=angle (frequency/angle), 0.1 is a factor to control the angle/frequency so that when the horizontal or x axis grid has 10 unit values the sinusoid shall show 1 cycle of cos(2pix) or sin(2pix) or 2pi radian of angle if b=1, c = phase and n is an arbitrary "hidden value" to multiply with 0.05*c to be 0.05*c*n that sets the animation phase of the sinusoid on the display space either to move left or right. It is shown in equation ( 8) and ( 9 The following equations from (10), (11), ( 12) and ( 13) are the byproducts of applying mathematical operation to the sinusoid to get tangent(2piX), AM functions, FM functions and harmonics of sinusoid as required in the future fitting works. Next is to use the legendary 'Taylor Series' function.…”

Section: Algorithm 4 Drawmentioning

confidence: 99%

New Optimized Adaptive Time Series IDS Classifier Algorithm: Beyond Deep Learning

Muda,

Yusof,

Alfawaz

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Adaptive time series Intrusion Detection System (IDS) Classifier is essential to detect realtime cyber-threats. Meanwhile, optimized hyperparameters on time series IDS classifier model will ensure swift detection. However, current studies on Time Series IDS classifier involve additional RNN-LSTM layers and multiple gates to optimize the training and feedback process. Notwithstanding, RNN-LSTM has powerful features to memorize data sequences. The nature of multiple complex hidden states in RNN-type model requires intensive training or epoch to achieve optimized loss function. This paper aims to go beyond conventional deep learning model by removing complex gated states and conventional hidden layers. The goal is to create an optimized adaptive time series classifier. The model leverages various fitting algorithms which include Sinusoidal, Linear, Power Function, Taylor Series and a new "Staircase" function that is introduced in this study. These functions adapt gradually to the real-time target distribution pattern. This will eliminate the need for feedback process to optimize hyperparameters. The model's performance is evaluated against the realistic benchmarked IDS dataset; a dataset that simulates recent malware attacks and has imbalanced distribution property. This property reflects a realistic low cyber-attack footprint. After 10 epoch over randomized stratified testing samples, the Mean Absolute Error (MAE) rate achieved almost 0.0% after a fitting process reached 100% as compared with the conventional LSTM model that achieved 17%.

show abstract

A Novel Reinforced Deep RNN–LSTM Algorithm: Energy Management Forecasting Case Study

Cited by 23 publications

References 23 publications

Robust Vehicle-to-Grid Energy Trading Method Based on Smart Forecast and Multi-Blockchain Network

Robust Vehicle-to-Grid Energy Trading Method Based on Smart Forecast and Multi-Blockchain Network

Energy Demand Forecasting and Optimizing Electric Systems for Developing Countries

New Optimized Adaptive Time Series IDS Classifier Algorithm: Beyond Deep Learning

Contact Info

Product

Resources

About