A Deep Learning Based Real-time Load Forecasting Method in Electricity Spot Market

Zhang, Qipei; Lu, Jixiang; Zhihong, Yang; Tu, Mengfu

doi:10.1088/1742-6596/1176/6/062068

Cited by 9 publications

(5 citation statements)

References 5 publications

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“…However, the learning algorithm of such a model is very slow and computationally expensive especially with nonlinear data and large datasets. The training rate could be accelerated through the use of advanced learning algorithms such as Deep Recurrent Neural Network (DRNN) which could eventually reduce the computational cost (Zhang et al 2019). Zhang et al (2020) proposed a Recurrent Neural Network (RNN)-based LF model to improve accuracy.…”

Section: Related Workmentioning

confidence: 99%

Short-Term Energy Load Forecasting Model with Sample Filtering

Kateule,

Sakala,

Tunga

2023

Tanz. J. Sci.

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Short-term energy load forecasting is a crucial task in the power smart grid, which enables the power utilities to understand the future energy demands and plans to attain the demand and supply equilibrium, thereby optimizing power deployment and reducing power losses. Several techniques have been implemented to enhance energy load forecasting. However, the nonlinear nature of the data collected in the smart grid makes it difficult to attain 100% energy load forecasting accuracy. For instance, the Deep Feedforward Neural Networks model based on Input Attention Mechanism and Hidden Connection Mechanism has a mean absolute percentage error of 3.17%; model based on Sequence to Sequence Recurrent Neural Network with Attention had a mean absolute percentage error of 2.7%. The model based on Deep Recurrent Neural Networks with Levenberg–Marquardt backpropagation algorithm had a mean absolute percentage error of 0.58; and Deep Feedforward Neural Network with sample weights model had 3.22 % as root mean squared error. To improve energy load forecasting accuracy, this work proposed a model based on Deep Recurrent Neural Networks and sample filtering, which provides an exhaustive elucidation for modelling a sophisticated stochastic relationship between the input and output features. Deep Recurrent Neural Networks have proven to be good at modelling the nonlinearities in data of different fields and are mostly used in energy load forecasting to reduce forecasting error and a high degree of overfitting. Sample filtering is achieved through the use of K-Means clustering which determines the number of clusters to be used in the model. Findings from the study showed that by employing Deep Recurrent Neural Networks and sample filtering, the short-term energy load forecasting accuracy is improved in reference to mean absolute percentage error and root mean squared error of 0.31% and 1.014, respectively. As a result of the reduction in error, the energy demand and supply chain equilibrium are enhanced, thereby optimizing power deployment and reducing power losses. Keywords: Machine learning, Neural networks, Sample filtering, Smart grid, Short-term energy forecasting

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Section: Related Workmentioning

confidence: 99%

Short-Term Energy Load Forecasting Model with Sample Filtering

Kateule,

Sakala,

Tunga

2023

Tanz. J. Sci.

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“…It was also found that LSTM is the most widely used ANN technique for load forecasting. For purposes such as improving feature selection [49], feature extraction [78], computational time [84], etc., LSTM has been found to provide more impactful outputs than other ANN techniques [40] [50] [53] (Table IV). BPNN was the next most used technique for load forecasting.…”

Section: B Different Ann Techniques In Deep Learning Based Load Forementioning

confidence: 99%

HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

et al. 2020

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Load forecasting is a vital part of smart grids for predicting the required electrical power using artificial intelligence (AI). Deep learning is broadly used for load forecasting in the smart grid using the artificial neural network (ANN). Generally, computing the deep learning in the smart grid requires massive data aggregation or centralization and significant computational time. This paper presents a survey of deep learning-based load forecasting techniques from 2015 to 2020. This survey discusses the studies based on their deep learning techniques, Distributed Deep Learning (DDL) techniques, Back Propagation (BP) based works, and non-BP based works in the load forecasting process. Consequent to the survey, it was determined that data aggregation dependency would be beneficial for reducing computational time in load forecasting. Therefore, 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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“…ANNs 18 retains excellent non-linear mapping capabilities, which enable it for power load forecasting. Recently many researchers have applied deep learning methods 9,19 for electricity load forecasting. Several other researchers have also applied machine learning models including Extreme Learning Machine Neural Network (ELMNN), 20 Generalized Regression Neural Network (GRNN) 21 and Support Vector Machine (SVM).…”

Section: State Of Artmentioning

confidence: 99%

Impact of Covid19 on electricity load in Haryana (India)

2020

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Summary As it is known that the whole world is battling against the Corona Virus Disease or COVID19 and trying their level best to stop the spread of this pandemic. To avoid the spread, several countries like China, Italy, Spain, America took strict measures like nationwide lockdown or by cordoning off the areas that were suspected of having risks of community spread. Taking cues from the foreign counterparts, the government of India undertook an important decision of nationwide full lockdown on March 25th which was further extended till May 4th, 2020 (47 days‐full lockdown). Looking at the current situation government of India pushed the lockdown further with eased curbs, divided the nation into green, orange and red zones, rapid testing of citizens in containment area, mandatory wearing of masks and following social distancing among others. The outbreak of the pandemic, has led to the large economic shock to the world which was never been experienced since decades. Moreover it brought a great uncertainty over the world wide electricity sector as to slow down the spread of the virus, many countries have issued restrictions, including the closure of malls, educational institutions, halting trains, suspending of flights, implemented partial or full lockdowns, insisted work from home to the employees. In this paper, the impact analysis of electricity consumption of state Haryana (India) is done using machine learning conventional algorithms and artificial neural network and electricity load forecasting is done for a week so as to aid the electricity board to know the consumption of the area pre hand and likewise can restrict the electricity production as per requirement. Thus, it will help power system to secure electricity supply and scheduling and reduce wastes since electricity is difficult to store. For this the dataset from regional electricity boards of Haryana that is, Dakshin Haryana Bijli Vitran Nigam and Uttar Haryana Bijli Vitran Nigam were analysed and electricity loads of state were predicted using python programming and as per result analysis it was observed that artificial neural network out performs conventional machine learning models.

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A Deep Learning Based Real-time Load Forecasting Method in Electricity Spot Market

Cited by 9 publications

References 5 publications

Short-Term Energy Load Forecasting Model with Sample Filtering

Short-Term Energy Load Forecasting Model with Sample Filtering

HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

Impact of Covid19 on electricity load in Haryana (India)

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