Journal ArticleDOI
Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid
TLDR
A novel hybrid short-term electric load forecasting model is proposed, an integrated framework of data pre-processing and feature selection module, training and forecasting module, and an optimization module that is validated by comparing it with four recent forecasting models like Bi-level, mutual information-based artificial neural network (MI-ANN), ANN-based accurate and fast converging (AFC- ANN), and long short- term memory (LSTM) in terms of accuracy and convergence rate.About:
This article is published in Applied Energy.The article was published on 2020-07-01. It has received 153 citations till now. The article focuses on the topics: Feature selection & Artificial neural network.read more
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Random Vector Functional Link Neural Network based Ensemble Deep Learning
TL;DR: Zhang et al. as discussed by the authors proposed an ensemble deep network (edRVFL) that can be regarded as a marriage of ensemble learning with deep learning, which can be obtained by training a single RVFL network once.
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Artificial Intelligence Techniques in Smart Grid: A Survey
Olufemi A. Omitaomu,Haoran Niu +1 more
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HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey
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TL;DR: A conceptual model of DDL for smart grids has been presented, where the HSIC (Hilbert-Schmidt Independence Criterion) Bottleneck technique has been incorporated to provide higher accuracy.
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Multi-temporal-spatial-scale temporal convolution network for short-term load forecasting of power systems
Linfei Yin,Jiaxing Xie +1 more
TL;DR: The simulation results show that the proposed multi-temporal-spatial-scale temporal convolutional network can obtain higher accuracy for the short-term load forecasting of power systems than other compared methods.
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Electrical load forecasting: A deep learning approach based on K-nearest neighbors
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References
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Journal Article
Dropout: a simple way to prevent neural networks from overfitting
TL;DR: It is shown that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.
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Deep Learning for Household Load Forecasting—A Novel Pooling Deep RNN
Heng Shi,Minghao Xu,Ran Li +2 more
TL;DR: A novel pooling-based deep recurrent neural network is proposed in this paper which batches a group of customers’ load profiles into a pool of inputs and could address the over-fitting issue by increasing data diversity and volume.
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Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks
TL;DR: A recurrent neural network model to make medium-to-long term predictions of electricity consumption profiles in commercial and residential buildings at one-hour resolution and uses the deep NN to perform imputation on an electricity consumption dataset containing segments of missing values is presented.
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Smart Electricity Meter Data Intelligence for Future Energy Systems: A Survey
Damminda Alahakoon,Xinghuo Yu +1 more
TL;DR: A comprehensive survey of smart electricity meters and their utilization is presented focusing on key aspects of the metering process, different stakeholder interests, and the technologies used to satisfy stakeholder interest.
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Short-Term Residential Load Forecasting Based on Resident Behaviour Learning
TL;DR: In this article, a long short-term memory-based deep-learning forecasting framework with appliance consumption sequences is proposed to address the volatile problem in residential load forecasting, which can be notably improved by including appliance measurements in the training data.