An Overview of Deep Learning Techniques for Short-Term Electricity Load Forecasting

ADEWUYI, Saheed; Aina, Segun; Lawal, Adebayo; Oluwaranti, Adeniran Ishola; Uzunuigbe, Moses

doi:10.35784/acs-2019-31

Cited by 3 publications

(10 citation statements)

References 16 publications

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“…As an evaluation method, the developed model was based on the three performance evaluation metrics in (Adewuyi et al, 2019).…”

Section: Evaluation Methodsmentioning

confidence: 99%

“…However, its applicability to quantitative data such as the electricity demands is sketchy. The internal representation and architecture of CNN is as in Adewuyi et al, (2019). Furthermore, in order to obtain a richer representation of the applied data, the hidden layer was stacked, so as to obtain multiple feature maps (Hosein & Hosein, 2017).…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

“…LSTM is another deep learning architecture utilised in this study. The introduction to its concepts is documented in our previous work (Adewuyi et al, 2019). However, in this paper, the LSTM deep method was defined as a sequential model characterised by 128 neurons of 3 stacked architecture.…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

“…We therefore investigated some of the deep learning techniques as relevant to the problem area (Adewuyi, Aina, Uzunuigbe, Lawal & Oluwaranti, 2019); and consequently, we exploited only a few models that recent studies have demonstrated to embody positive influence on the STLF research. However, we observed that most of these studies in the problem area were mostly done by applying the temperate climate datasets (Bouktif, Ali, Ali & Mohamed, 2018;Ghullam & Angelos, 2017;Hussein, 2018;Kuo & Huang, 2018;Stuart & Norvig, 2013;Yi, Jie, Yanhua & Caihong, 2013).…”

Section: Introductionmentioning

confidence: 99%

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A Deep Learning Model for Electricity Demand Forecasting Based on a Tropical Data

ADEWUYI

Aina

Oluwaranti

2020

acs

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Electricity demand forecasting is a term used for prediction of users’ consumption on the grid ahead of actual demand. It is very important to all power stakeholders across levels. The power players employ electricity demand forecasting for sundry purposes. Moreover, the government’s policy on its market deregulation has greatly amplified its essence. Despite numerous studies on the subject using certain classical approaches, there exists an opportunity for exploration of more sophisticated methods such as the deep learning (DL) techniques. Successful researches about DL applications to com¬puter vision, speech recognition, and acoustic computing problems are motivation. However, such researches are not sufficiently exploited for electricity demand forecasting using DL methods. In this paper, we considered specific DL techniques (LSTM, CNN, and MLP) to short-term load fore¬casting problems, using tropical institutional data obtained from a Transmission Company. We also test how accurate are predictions across the techniques. Our results relatively revealed models appropriateness for the problem.

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“…As an evaluation method, the developed model was based on the three performance evaluation metrics in (Adewuyi et al, 2019).…”

Section: Evaluation Methodsmentioning

confidence: 99%

Section: Convolutional Neural Networkmentioning

confidence: 99%

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Deep Learning Model for Electricity Demand Forecasting Based on a Tropical Data

ADEWUYI

Aina

Oluwaranti

2020

acs

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“…These methods often require the time series data to be stationary (i.e., constant mean, variance, and serial correlation). While these approaches can handle univariate data, they are single-step forecast models that necessitate extensive preprocessing and explicit definitions of input characteristics [23]. Machine learning models such as Support Vector Machines (SVM) [24][25][26], Bayesian Belief Networks [20,27], and Principal Component Analysis (PCA) [15,28] have also been employed in power load forecasting.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks for Power Load Prediction in Critical Infrastructure: A Comparative Case Study

Aliyari,

Ayele

2023

ASI

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This article aims to assess the effectiveness of state-of-the-art artificial neural network (ANN) models in time series analysis, specifically focusing on their application in prediction tasks of critical infrastructures (CIs). To accomplish this, shallow models with nearly identical numbers of trainable parameters are constructed and examined. The dataset, which includes 120,884 hourly electricity consumption records, is divided into three subsets (25%, 50%, and the entire dataset) to examine the effect of increasing training data. Additionally, the same models are trained and evaluated for univariable and multivariable data to evaluate the impact of including more features. The case study specifically focuses on predicting electricity consumption using load information from Norway. The results of this study confirm that LSTM models emerge as the best-performed model, surpassing other models as data volume and feature increase. Notably, for training datasets ranging from 2000 to 22,000 instances, GRU exhibits superior accuracy, while in the 22,000 to 42,000 range, LSTM and BiLSTM are the best. When the training dataset is within 42,000 to 360,000, LSTM and ConvLSTM prove to be good choices in terms of accuracy. Convolutional-based models exhibit superior performance in terms of computational efficiency. The convolutional 1D univariable model emerges as a standout choice for scenarios where training time is critical, sacrificing only 0.000105 in accuracy while a threefold improvement in training time is gained. For training datasets lower than 22,000, feature inclusion does not enhance any of the ANN model’s performance. In datasets exceeding 22,000 instances, ANN models display no consistent pattern regarding feature inclusion, though LSTM, Conv1D, Conv2D, ConvLSTM, and FCN tend to benefit. BiLSTM, GRU, and Transformer do not benefit from feature inclusion, regardless of the training dataset size. Moreover, Transformers exhibit inefficiency in time series forecasting due to their permutation-invariant self-attention mechanism, neglecting the crucial role of sequence order, as evidenced by their poor performance across all three datasets in this study. These results provide valuable insights into the capabilities of ANN models and their effective usage in the context of CI prediction tasks.

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Untitled

2022

IJEER

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Research on electricity load forecasting has been well circulated in journals. However, this was not particularly well done in the tropics. After all, forecasting electricity loads has been established to vary along climatic regions owing to different weather conditions, with the consequential effect of contrasting load requirements. This characteristic change has triggered the purport of this study for a while. Since the study began, as this is only an extension of previously done works by this team, deep architectures have been found more reliable than the classical models for load forecasting. As a result, in this study, an unsupervised deep learning architecture namely Stacked Autoencoder (SAE) was built for and applied on a 3-year historic electricity consumption and meteorological data for day-ahead prediction of electricity consumption of a tropical region. Consequently, the developed unsupervised (SAE) model demonstrated good results on both validation and test data, and its prediction cost was very minimal.

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An Overview of Deep Learning Techniques for Short-Term Electricity Load Forecasting

Cited by 3 publications

References 16 publications

A Deep Learning Model for Electricity Demand Forecasting Based on a Tropical Data

A Deep Learning Model for Electricity Demand Forecasting Based on a Tropical Data

Application of Artificial Neural Networks for Power Load Prediction in Critical Infrastructure: A Comparative Case Study

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