An effective deep learning neural network model for short‐term load forecasting

Li, Ning; Wang, Lu; Li, Xinquan; Zhu, Qing

doi:10.1002/cpe.5595

Cited by 15 publications

(8 citation statements)

References 64 publications

(100 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Related Workmentioning

confidence: 99%

“…The use of convolutional neural networks (CNN) can be also found in [ 2 , 10 ] as a useful method to predict power load. In [ 10 ], the authors defined new loss functions as main novelty and outperformed results by LSTM, ANN and SVR. In [ 2 ], the CNN used a two-dimensional input with historical load data and exogenous variables for both one-step-ahead (15 min) and 96-step-ahead (24 h).…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A deep LSTM network for the Spanish electricity consumption forecasting

Torres

Martínez‐Álvarez

Troncoso

2022

Neural Comput & Applic

View full text Add to dashboard Cite

Nowadays, electricity is a basic commodity necessary for the well-being of any modern society. Due to the growth in electricity consumption in recent years, mainly in large cities, electricity forecasting is key to the management of an efficient, sustainable and safe smart grid for the consumer. In this work, a deep neural network is proposed to address the electricity consumption forecasting in the short-term, namely, a long short-term memory (LSTM) network due to its ability to deal with sequential data such as time-series data. First, the optimal values for certain hyper-parameters have been obtained by a random search and a metaheuristic, called coronavirus optimization algorithm (CVOA), based on the propagation of the SARS-Cov-2 virus. Then, the optimal LSTM has been applied to predict the electricity demand with 4-h forecast horizon. Results using Spanish electricity data during nine years and half measured with 10-min frequency are presented and discussed. Finally, the performance of the proposed LSTM using random search and the LSTM using CVOA is compared, on the one hand, with that of recently published deep neural networks (such as a deep feed-forward neural network optimized with a grid search) and temporal fusion transformers optimized with a sampling algorithm, and, on the other hand, with traditional machine learning techniques, such as a linear regression, decision trees and tree-based ensemble techniques (gradient-boosted trees and random forest), achieving the smallest prediction error below 1.5%.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A deep LSTM network for the Spanish electricity consumption forecasting

Torres

Martínez‐Álvarez

Troncoso

2022

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…Traditional statistical methods include linear regression [9], the gray model method [10], the fuzzy prediction method [11], and the autoregressive integrated sliding average model [12]. These methods are simple and easy to implement, but they require a high level of raw data processing and time series stability.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Based on a Hybrid Neural Network and Phase Space Reconstruction

et al. 2022

View full text Add to dashboard Cite

Most current short-term load forecasting models have difficulty in simultaneously taking into account the time-series nature of load data, the non-linear characteristics, and the ineffectiveness of extracting potential high-dimensional features from historical series. To solve this problem, we propose a hybrid neural algorithm model (DPCL).In the DPCL, we use convolutional neural networks to obtain the high-dimensional spatial features of the phase space reconstruction of the load time series. Then, we combine the obtained high-dimensional spatial features with the external influence features extracted in the Pearson correlation analysis. Long and short-term memory networks retrieve Spatio-temporal features through the connection layer and obtain prediction results. In addition, there are problems of network gradient degradation and overfitting during the training process, We use an improved differential evolutionary algorithm to optimize the topology and time step of the hybrid neural network. We use the public dataset of a European utility and the loaded dataset of a Chinese mathematical competition as practical arithmetic examples. Experiments have higher prediction accuracy and faster prediction speed compared with other traditional algorithms.

show abstract

“…The authors used the Huber function to perform robust estimation in electrical power engineering [24]. The researchers discussed the effectiveness of the MAE and observed how the error varied when they trained a deep learning model to predict energy load [25]. The scholars studied the distributional loss for regression from the perspective of effective optimization, examining the MAE and MSE [26].…”

Section: Introductionmentioning

confidence: 99%

Blood Pressure and Heart Rate Measurements Using Photoplethysmography with Modified LRCN

Mostafa¹,

Idris²,

Kaur³

2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

In this study, single-channel photoplethysmography (PPG) signals were used to estimate the heart rate (HR), diastolic blood pressure (DBP), and systolic blood pressure (SBP). A deep learning model was proposed using a long-term recurrent convolutional network (LRCN) modified from a deep learning algorithm, the convolutional neural network model of the modified inception deep learning module, and a long short-term memory network (LSTM) to improve the model's accuracy of BP and HR measurements. The PPG data of 1,551 patients were obtained from the University of California Irvine Machine Learning Repository. How to design a filter of PPG signals and how to choose the loss functions for deep learning model were also discussed in the study. Finally, the stability of the proposed model was tested using a 10-fold cross-validation, with an MAE ± SD of 2.942 ± 5.076 mmHg for SBP, 1.747 ± 3.042 mmHg for DBP, and 1.137 ± 2.463 bpm for the HR. Compared with its existing counterparts, the model entailed less computational load and was more accurate in estimating SBP, DBP, and HR. These results established the validity of the model.

show abstract

An effective deep learning neural network model for short‐term load forecasting

Cited by 15 publications

References 64 publications

A deep LSTM network for the Spanish electricity consumption forecasting

A deep LSTM network for the Spanish electricity consumption forecasting

Short-Term Load Forecasting Based on a Hybrid Neural Network and Phase Space Reconstruction

Blood Pressure and Heart Rate Measurements Using Photoplethysmography with Modified LRCN

Contact Info

Product

Resources

About