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Related papers: RNN(p) for Power Consumption Forecasting

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In this paper, we investigate the use of prediction-adaptation-correction recurrent neural networks (PAC-RNNs) for low-resource speech recognition. A PAC-RNN is comprised of a pair of neural networks in which a {\it correction} network uses…

Computation and Language · Computer Science 2018-12-06 Yu Zhang , Ekapol Chuangsuwanich , James Glass , Dong Yu

Physical Neural Networks (PNN) are promising platforms for next-generation computing systems. However, recent advances in digital neural network performance are largely driven by the rapid growth in the number of trainable parameters and,…

Machine Learning · Computer Science 2025-11-19 Kohei Tsuchiyama , Andre Roehm , Takatomo Mihana , Ryoichi Horisaki

Recurrent Neural Networks (RNNs) are becoming increasingly important for time series-related applications which require efficient and real-time implementations. The two major types are Long Short-Term Memory (LSTM) and Gated Recurrent Unit…

Computer Vision and Pattern Recognition · Computer Science 2018-12-19 Zhe Li , Caiwen Ding , Siyue Wang , Wujie Wen , Youwei Zhuo , Chang Liu , Qinru Qiu , Wenyao Xu , Xue Lin , Xuehai Qian , Yanzhi Wang

Electricity load consumption may be extremely complex in terms of profile patterns, as it depends on a wide range of human factors, and it is often correlated with several exogenous factors, such as the availability of renewable energy and…

Machine Learning · Computer Science 2025-02-03 Aleksei Kychkin , Georgios C. Chasparis

This paper aims to discuss and analyze the potentialities of Recurrent Neural Networks (RNN) in control design applications. The main families of RNN are considered, namely Neural Nonlinear AutoRegressive eXogenous, (NNARX), Echo State…

Systems and Control · Electrical Eng. & Systems 2022-05-11 Fabio Bonassi , Marcello Farina , Jing Xie , Riccardo Scattolini

Forecasting time series data is a critical area of research with applications spanning from stock prices to early epidemic prediction. While numerous statistical and machine learning methods have been proposed, real-life prediction problems…

Machine Learning · Statistics 2023-12-05 Madhurima Panja , Tanujit Chakraborty , Uttam Kumar , Abdenour Hadid

Forecasting electricity demand is increasingly challenging as energy systems become more decentralized and intertwined with renewable sources. Graph Neural Networks (GNNs) have recently emerged as a powerful paradigm to model spatial…

Machine Learning · Computer Science 2025-11-04 Eloi Campagne , Yvenn Amara-Ouali , Yannig Goude , Itai Zehavi , Argyris Kalogeratos

This study applies recurrent neural networks (RNNs), which are known for its ability to process sequential information, to model the spatio-temporal dynamics of land use change (LUC) and to forecast annual land use maps of the city of…

Applications · Statistics 2018-05-08 Guodong Du , Liang Yuan , Kong Joo Shin , Shunsuke Managi

Accurate power load forecasting is crucial for improving energy efficiency and ensuring power supply quality. Considering the power load forecasting problem involves not only dynamic factors like historical load variations but also static…

Machine Learning · Computer Science 2024-09-27 Chao Min , Yijia Wang , Bo Zhang , Xin Ma , Junyi Cui

The advantage of recurrent neural networks (RNNs) in learning dependencies between time-series data has distinguished RNNs from other deep learning models. Recently, many advances are proposed in this emerging field. However, there is a…

Neural and Evolutionary Computing · Computer Science 2016-02-16 Hojjat Salehinejad

Recommender systems objectives can be broadly characterized as modeling user preferences over short-or long-term time horizon. A large body of previous research studied long-term recommendation through dimensionality reduction techniques…

Information Retrieval · Computer Science 2018-07-25 Kiewan Villatel , Elena Smirnova , Jérémie Mary , Philippe Preux

Recent research demonstrate that prediction of time series by recurrent neural networks (RNNs) based on the noisy input generates a smooth anticipated trajectory. We examine the internal dynamics of RNNs and establish a set of conditions…

Machine Learning · Computer Science 2020-10-07 Boris Rubinstein

Recurrent neural networks (RNNs) are nonlinear dynamical models commonly used in the machine learning and dynamical systems literature to represent complex dynamical or sequential relationships between variables. More recently, as deep…

Methodology · Statistics 2018-02-08 Patrick L. McDermott , Christopher K. Wikle

Autoregressive (AR) models remain widely used in time series analysis due to their interpretability, but convencional parameter estimation methods can be computationally expensive and prone to convergence issues. This paper proposes a…

Machine Learning · Statistics 2026-03-20 Anaísa Lucena , Ana Martins , Armando J. Pinho , Sónia Gouveia

The high variability of weather parameters is making photovoltaic energy generation intermittent and narrowly controllable. Threatened by the sudden discontinuity between the load and the grid, energy management for smart grid systems…

Signal Processing · Electrical Eng. & Systems 2019-10-23 Mohamed Massaoudi , Ines Chihi , Lilia Sidhom , Mohamed Trabelsi , Shady S. Refaat , Fakhreddine S. Oueslati

We introduce a novel energy formulation for Plug- and-Play (PnP) image recovery. Traditional PnP methods that use a convolutional neural network (CNN) do not have an energy based formulation. The primary focus of this work is to introduce…

Image and Video Processing · Electrical Eng. & Systems 2023-02-24 Jyothi Rikabh Chand , Mathews Jacob

Short-term traffic flow prediction is one of the crucial issues in intelligent transportation system, which is an important part of smart cities. Accurate predictions can enable both the drivers and the passengers to make better decisions…

Machine Learning · Computer Science 2019-01-31 Alireza Nejadettehad , Hamid Mahini , Behnam Bahrak

Renewable sources of energy are the future due to the environmental problems caused by non-renewable sources to produce energy. The biggest issue with renewable energy sources is that the power produced by devices such as PV solar panels…

Signal Processing · Electrical Eng. & Systems 2022-10-24 Rohaib Bhatti , Ali John Naqvi , Abdullah Tauqeer

Transformers have surpassed RNNs in popularity due to their superior abilities in parallel training and long-term dependency modeling. Recently, there has been a renewed interest in using linear RNNs for efficient sequence modeling. These…

Computation and Language · Computer Science 2023-11-09 Zhen Qin , Songlin Yang , Yiran Zhong

The non-stationarity characteristic of the solar power renders traditional point forecasting methods to be less useful due to large prediction errors. This results in increased uncertainties in the grid operation, thereby negatively…

Machine Learning · Computer Science 2020-09-15 Sakshi Mishra , Praveen Palanisamy