Showing 1-25 of 25,591 resultsfor "Index Terms":Short-term Prediction
"Index Terms":Short-term Prediction
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GA-CNN-LSTM Based Temperature and Humidity Prediction Method
2024 11th International Forum on Electrical Engineering and Automation (IFEEA)
Year: 2024 | Conference Paper |
In order to avoid the significant impact of the “three highs” climate on the operation of power system equipment, this paper introduces genetic algorithm (GA) into the hybrid prediction model structure of convolutional neural network (CNN) and multi-layer long short-term memory (LSTM) network, and proposes GA-CNN-LSTM model to achieve the prediction of temperature and humidity. Genetic algorithm i...Show More
Advanced Stock Price Prediction with xLSTM-Based Models: Improving Long-Term Forecasting
2024 11th International Conference on Soft Computing & Machine Intelligence (ISCMI)
Year: 2024 | Conference Paper |
Stock price prediction has long been a critical area of research in financial modeling. The inherent complexity of financial markets, characterized by both short-term fluctuations and long-term trends, poses significant challenges in accurately capturing underlying patterns. While Long Short-Term Memory (LSTM) networks have shown strong performance in short-term stock price prediction, they strugg...Show More
Short-term photovoltaic power generation prediction based on VMD-IGWO-LSTM
Zhang Gui Chen;Tian Lei;Zhou Jing Hua;Boixanov Zailobiddin Urazali Ugli;Azizbek Rakhmonov Yigitali Ugli
2024 IEEE 4th International Conference on Digital Twins and Parallel Intelligence (DTPI)
Year: 2024 | Conference Paper |
In order to improve the prediction accuracy of photovoltaic power generation, a short-term photovoltaic power generation prediction method based on variational mode decomposition (VMD) and improved grey wolf optimization algorithm (IGWO) to optimize long short term memory (LSTM) neural network is proposed. The multi-dimensional photovoltaic feature data is decomposed into several intrinsic modes a...Show More
Short-Term Wind Power Prediction Based on IAVOA-LSTM Neural Network
2024 7th International Conference on Computer Information Science and Application Technology (CISAT)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
The accurate prediction of wind power generation is critical for the stable operation and dispatch of power systems. This paper presents a wind power prediction method that employs an Improved African Vultures Optimization Algorithm Optimized Long Short-Term Memory (IAVOA-LSTM) network. Initially, wind data is preprocessed to construct input and output sample sets for the LSTM network. Subsequentl...Show More
Short-Term Electricity Load Forecasting Model Based on Bidirectional Long Short-Term Memory Network with Adaptive Boosting
2024 11th International Forum on Electrical Engineering and Automation (IFEEA)
Year: 2024 | Conference Paper |
Short-term load forecasting plays a vital role in the operation and management of power systems. While traditional forecasting models are capable of predicting general load trends, their predictions often exhibit significant deviations from actual values in specific regions, particularly in cases of rapid load fluctuations. To address this issue and minimize prediction errors, this paper introduce...Show More
Short-term Electricity Load Prediction and Carbon Emissions Based on Synchrosqueezing Wavelet Transform and Nested Long Short-Term Memory
Zhonghao Li;Yuanshang Ji;Jinjian Wang;Lei Fang;Zijie Yang;Chenggong Zhang;Bingtao Guo;Bin He
2024 3rd International Conference on Smart Grids and Energy Systems (SGES)
Year: 2024 | Conference Paper |
Accurately predicting that short-term electricity load and carbon emissions are essential for optimizing energy structure, reducing carbon emissions and achieving sustainable development. This article proposes a short-term electricity load carbon emission prediction method based on Synchrosqueezing Wavelet Transform (SWT) and Nested Long Short-Term Memory (NLSTM) neural network. First of all, by i...Show More
Enhanced Short-Term Load Forecasting Using BILSTM and AdaBoost with Error Correction
2024 China Automation Congress (CAC)
Year: 2024 | Conference Paper |
Short-term load forecasting is crucial for the safety of the power grid and the operation and maintenance of electric fields. However, existing methods have limitations in capturing the complex nonlinear characteristics of loads, resulting in low prediction accuracy. To improve the predictability of load sequences, this paper proposes a load forecasting model based on Bidirectional Long Short-Term...Show More
To improve the accuracy of short term traffic flow prediction and to solve the problems of nonlinearity of short term traffic flow, more noise in the data, and more difficult to determine the parametes of long short term memory networks, a combined traffic flow prediction model based on variational modal decomposition (VMD) and improved dung beetle optimization-long short term memory network (IDBO...Show More
Research on Fault Warning Algorithm for Coal Mills Based on CNN-LSTM-Attention
Yuzhi Huang;Haibin Liu;Jun Li;Lin Gao;Honghui Wang;Xiaolong Sheng;Peng Chang;Chengwen Wang;Jiachen Cai
2024 IEEE 4th International Conference on Digital Twins and Parallel Intelligence (DTPI)
Year: 2024 | Conference Paper |
As the frequent failure issues of coal mills in thermal power plants operating under harsh conditions become increasingly prominent, it is particularly important to effectively predict faults and assess the remaining useful life of coal mills. This paper proposes a fault warning method for coal mills based on CNN-LSTM-Attention. This method employs deep learning techniques and selects data related...Show More
Short-term wind power prediction based on a CEEMDAN-SSA-LSTM-Attention mechanism
Miao Yu;Jianqun Sun;Jingxuan Hu;Shouzhi Zhang;Jingjing Wei;Yixiao Wu
2024 7th International Conference on Electrical Engineering and Green Energy (CEEGE)
Year: 2024 | Conference Paper |
Accurate wind power prediction facilitates power system operation, peak regulation, security analysis, and energy conservation and reduction. In this paper, we propose a short-term prediction model for wind power based on a Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-Sparrow Search Algorithm (SSA)-Attention Mechanism-Long Short Term Memory Neural Network (LSTM...Show More
Short-Term Traffic Prediction Using Deep Learning Long Short-Term Memory: Taxonomy, Applications, Challenges, and Future Trends
Anwar Khan;Mostafa M. Fouda;Dinh-Thuan Do;Abdulaziz Almaleh;Atiq Ur Rahman
This paper surveys the short-term road traffic forecast algorithms based on the long-short term memory (LSTM) model of deep learning. The algorithms developed in the last three years are studied and analyzed. This provides an in-depth and thorough description of the algorithms rather than their marginal description as performed in the existing surveys that focus on general deep learning algorithms...Show More
Wind power prediction model based on deep learning neural network
2023 IEEE 3rd International Conference on Electronic Technology, Communication and Information (ICETCI)
Year: 2023 | Conference Paper |
Wind power generation has strong randomness and volatility, and accurate prediction of wind power can improve the safety and reliability of grid operation. To further improve the accuracy of short-term wind power prediction, a long short-term memory (LSTM) short-term wind power prediction method is proposed. First, we selected a wind power plant, from 0:00 on July 1, 2022, to 22:38 on July 8, 2022...Show More
Short-Term Traffic Flow Prediction Based on XGBoost
2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)
Year: 2018 | Conference Paper |
Cited by: Papers (54)
Fast and accurate short-term traffic flow prediction is an important precondition for traffic analysis and control. Due to the fact that the short-term traffic flow has nonlinear characteristic and changes randomly, concurrent computation is difficult for traditional machine learning algorithms. In this paper, a traffic flow prediction model combining wavelets decomposition and reconstruction with...Show More
A Short-Term Traffic Flow Prediction Method Based on Kernel Extreme Learning Machine
2018 IEEE International Conference on Big Data and Smart Computing (BigComp)
Year: 2018 | Conference Paper |
Cited by: Papers (12)
Real-time and accurate traffic flow prediction plays an important role in ITS (Intelligent Transport System). Extreme learning machine (ELM) has proven to be an efficient and effective learning paradigm for a wide field. With the method of kernel function instead of the hidden layer, Kernel-ELM overcame the problem of variation caused by randomly assigned weights. In order to improve the accuracy ...Show More
Short-term wind power prediction model based on PCA-GA-LSTM neural network
2023 IEEE 6th International Electrical and Energy Conference (CIEEC)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
In order to improve the prediction accuracy of short-term wind power, a PCA-GA-LSTM short-term wind power combination prediction algorithm based on NWP is proposed. First, principal component analysis is used to reduce the dimension and denoise NWP sample data, and then genetic algorithm is used to train and optimize the model parameters of short-term memory artificial neural network (LSTM), such ...Show More
The short-term traffic flow prediction based on neural network
2010 2nd International Conference on Future Computer and Communication
Year: 2010 | Conference Paper |
Cited by: Papers (26)
As we all know, to predict the short-term traffic flow accurately and efficiently is the premise and key of traffic management and control. Based on these existing study, this paper selected BP neural network model in which the traffic flow difference was taken as the input parameter, applied the thought of dynamic rolling prediction to design a new short-term traffic flow prediction method, and w...Show More
Improved Long Short-Term Memory-Based Periodic Traffic Volume Prediction Method
Yuguang Chen;Jincheng Guo;Hongbin Xu;Jintao Huang;Linyong Su
In response to the problem of fixed time intervals for short-term traffic flow prediction, which fails to meet the requirements of traffic signal control based on traffic cycle signals, this paper proposes an improved long short-term memory-based method for periodic traffic volume prediction. The method presented in this study involves improvements to the Long Short-Term Memory (iLSTM) and Bidirec...Show More
Multi Step Temporal Spectrum Occupancy Prediction using Deep Learning
Niranjana Radhakrishnan;Sithamparanathan Kandeepan;Xinghuo Yu;Gianmarco Baldini
2024 17th International Conference on Signal Processing and Communication System (ICSPCS)
Year: 2024 | Conference Paper |
Spectrum occupancy prediction offers many advantages in Dynamic Spectrum Access (DSA) type applications and has been performed using conventional algorithms such as linear predictors, Bayesian prediction, etc. Deep learning (DL) algorithms such as Long Short Term Memory (LSTMs), Convolutional Neural Networks (CNNs), and its variants have been increasingly adopted for these applications over recent...Show More
Short-term wind speed prediction model of VMD-FOAGRNN based on Lorenz disturbance
2019 IEEE Sustainable Power and Energy Conference (iSPEC)
Year: 2019 | Conference Paper |
Cited by: Papers (1)
Accurate wind speed prediction is the key to wind energy development and utilization. However, due to the intermittent, random and chaotic nature of wind speed, wind speed prediction has the problem of low accuracy. So a short-term wind speed prediction model of VMD-FOAGRNN based on Lorenz perturbation is proposed in this paper to predict wind speed. Firstly, the actual wind speed sequence is deco...Show More
An Improved Attention-based BiLSTM for PM2.5 Prediction in Air Environment
2024 International Conference on Networking, Sensing and Control (ICNSC)
Year: 2024 | Conference Paper |
The accurate prediction of PM2.5 concentrations in the air is crucial for air quality control and pollution prevention. However, the complexity of air environment data and its time-series characteristics pose significant challenges to this prediction task. This study first employs a random forest algorithm for feature selection to identify key features as input variables. Subsequently, a method co...Show More
BiLSTM Model for Electricity Consumption Prediction: A Comparative Study with Traditional Machine Learning Approaches
2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON)
Year: 2024 | Conference Paper |
The reliability of power systems depends on accurate electricity load forecasting, as the demand for residential electricity rises annually as a result of infrastructure expansion and population expansion. While some traditional models based on machine learning have shown improvement in many areas, their accuracy in predicting energy consumption is still not up to par. Residential electricity cons...Show More
Short Term Wind Power Prediction Based on VMD-SAO-LSTM
Ahezhuli Wumuerwuzhake;Maimaitireyimu Abulizi;Xukun Wu;Huaiheng Xue
2024 4th International Conference on Smart Grid and Energy Internet (SGEI)
Year: 2024 | Conference Paper |
To address the issue of low accuracy in short-term wind power prediction, a combined prediction model integrating parameter-optimized Variational Mode Decomposition (VMD), the Snow Ablation Optimizer (SAO), and a Long Short-Term Memory (LSTM) network is proposed. First, the rime-ice Optimization Algorithm (RIME) is employed to optimize the parameters of VMD, which is then used to decompose the win...Show More
Multi-level collaborative short-term load forecasting
2022 25th International Conference on Electrical Machines and Systems (ICEMS)
Year: 2022 | Conference Paper |
Considering that the campus electric load presents diversity, the accuracy and rapidity of load prediction will directly affect the safety and economy of the electric power system. In order to better explore the effective information contained in the electric load data, improve the accuracy of short-term load prediction, and analyze the campus electricity consumption pattern, this paper proposes a...Show More
Wind Power Prediction Based on LSTM Networks and Phase Space Theory
2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS)
Year: 2024 | Conference Paper |
To address the chaotic nature of wind power time series, a wind power prediction technique leveraging Long Short- Term Memory (LSTM) networks and phase space theory was introduced. The wind power sequence's delay time and embedding dimension are established through the use of the mutual information method and the GP algorithm. Following this, the wind power data is reconstructed based on the Taken...Show More
Short-term Power Load Forecasting Based on Particle Swarm Optimization Long Short-term Memory Neural Network
2023 IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA)
Year: 2023 | Conference Paper |
Cited by: Papers (4)
Accurate power load prediction at different periods can provide an essential basis for energy consumption reduction and power scheduling. Particle swarm optimization (PSO) and long short-term memory (LSTM) neural networks were introduced into the forecasting method of electric power load. First, aiming at the problem that it is difficult to select the LSTM hyper-parameters, hyper-parameters includ...Show More
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