A Deep Learning CNN and AI-Tuned SVM for Electricity Consumption Forecasting: Multivariate Time Series Data

Chan, Steve; Oktavianti, Ika; Puspita, Verlly

doi:10.1109/iemcon.2019.8936260

Cited by 28 publications

(10 citation statements)

References 15 publications

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“…In the literature, grids, smart grids, and microgrids relate to both grid control and demand-side management. DSOs can use grid load predictions for more efficient energy scheduling and locating potential volatile areas in their electricity net, thereby improving their operation quality (Chan et al, 2019;Shikulskaya et al, 2020). For demand-side management, smart grids can leverage demand response measures but require accurate predictions to do so efficiently (Krishnan et al, 2020;Kaur et al, 2019;Bruno et al, 2018).…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, some neural networks are combined with conventional methods to improve performance. Building hybrid models increases performance metrics substantially compared to its non-hybrid subparts (Chan et al, 2019;Krishnan et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

“…In the literature, hybrid models are compared to conventional methods and single neural networks with remarkable results (Chan et al, 2019;Krishnan et al, 2020;Dudek et al, 2020). Models must be accurate throughout the year because DSOs depend on the predictions for their grid operation.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

A scoping review of deep neural networks for electric load forecasting

Vanting

Jôrgensen

2021

Energy Inform

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The increasing dependency on electricity and demand for renewable energy sources means that distributed system operators face new challenges in their grid. Accurate forecasts of electric load can solve these challenges. In recent years deep neural networks have become increasingly popular in research, and researchers have carried out many experiments to create the most accurate deep learning models. Players in the energy sector can exploit the increasing amount of energy-related data collected from smart meters to improve the grid’s operating quality. This review investigates state-of-the-art methodologies relating to energy load forecasting using deep neural networks. A thorough literature search is conducted, which outlines and analyses essential aspects regarding deep learning load forecasts in the energy domain. The literature suggests two main perspectives: demand-side management and grid control on the supply side. Each perspective has multiple applications with its challenges to achieve accurate forecasts; households, buildings, and grids. This paper recommends using a hybrid deep learning multivariate model consisting of a convolutional and recurrent neural network based on the scoping review. The suggested input variables should be historical consumption, weather, and day features. Combining the convolutional and recurrent networks ensures that the model learns as many repeating patterns and features in the data as possible.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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A scoping review of deep neural networks for electric load forecasting

Vanting

Jôrgensen

2021

Energy Inform

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“…But at this time there is a new method, namely the Deep Learning method, which can process training quickly, at this time there are also many researchers conducting research on predictions using the method as reflected by Kim et al regarding the prediction of household electricity consumption using CNN-LSTM Hybrid Network [12], the proposed method can be quickly and accurately in predicting irregular energy consumption trends in the dataset of household power consumption. However, because the proposed method was processed earlier by the sliding window algorithm [13], this caused a prediction delay in the actual data [14], in other studies carried out by Young-Jun in electrical energy forecasting By comparing the models contained in the Deep Learning [15] including the LSTM, Gru, and SEQ2SEQ models with the results of the LSTM experiment get the best results with RMSE 0.96 [16], but this value is not good enough to use the actual data to use seasonal data features and Long term in forecasting more accurate electrical energy.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Electrical Energy Consumption Using LSTM Algorithm with Teacher Forcing Technique

Riady

Sen²

2021

JISA

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Electrical energy is an important foundation in world economic growth, therefore it requires an accurate prediction in predicting energy consumption in the future. The methods that are often used in previous research are the Time Series and Machine Learning methods, but recently there has been a new method that can predict energy consumption using the Deep Learning Method which can process data quickly for training and testing. In this research, the researcher proposes a model and algorithm which contained in Deep Learning, that is Multivariate Time Series Model with LSTM Algorithm and using Teacher Forcing Technique for predicting electrical energy consumption in the future. Because Multivariate Time Series Model and LSTM Algorithm can receive input with various conditions or seasons of electrical energy consumption. Teacher Forcing Technique is able lighten up the computation so that it can training and testing data quickly. The method used in this study is to compare Teacher Forcing LSTM with Non-Teacher Forcing LSTM in Multivariate Time Series model using several activation functions that produce significant differences. TF value of RMSE 0.006, MAE 0.070 and Non-TF has RMSE and MAE values of 0.117 and 0.246. The value of the two models is obtained from Sigmoid Activation and the worst value of the two models is in the Softmax activation function, with TF values is RMSE 0.423, MAE 0.485 and Non-TF RMSE 0.520, MAE 0.519.

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“…Time series forecasting is an important problem in data mining with many real-world applications including finance [1]- [4], weather forecasting [5], [6], power consumption monitoring [7], [8], industrial maintenance [9], [10], occupancy monitoring in smart buildings [11], [12], and many others. Recently, deep learning (DL) models showed tremendous success in analyzing time series data [1], [13] when compared to the other traditional methods.…”

Section: Introductionmentioning

confidence: 99%

Adversarial Examples in Deep Learning for Multivariate Time Series Regression

Mode¹,

Hoque²

2020

Preprint

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Multivariate time series (MTS) regression tasks are common in many real-world data mining applications including finance, cybersecurity, energy, healthcare, prognostics, and many others. Due to the tremendous success of deep learning (DL) algorithms in various domains including image recognition and computer vision, researchers started adopting these techniques for solving MTS data mining problems, many of which are targeted for safety-critical and cost-critical applications. Unfortunately, DL algorithms are known for their susceptibility to adversarial examples which also makes the DL regression models for MTS forecasting also vulnerable to those attacks. To the best of our knowledge, no previous work has explored the vulnerability of DL MTS regression models to adversarial time series examples, which is an important step, specifically when the forecasting from such models is used in safetycritical and cost-critical applications. In this work, we leverage existing adversarial attack generation techniques from the image classification domain and craft adversarial multivariate time series examples for three state-of-the-art deep learning regression models, specifically Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). We evaluate our study using Google stock and household power consumption dataset. The obtained results show that all the evaluated DL regression models are vulnerable to adversarial attacks, transferable, and thus can lead to catastrophic consequences in safety-critical and cost-critical domains, such as energy and finance.

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A Deep Learning CNN and AI-Tuned SVM for Electricity Consumption Forecasting: Multivariate Time Series Data

Cited by 28 publications

References 15 publications

A scoping review of deep neural networks for electric load forecasting

A scoping review of deep neural networks for electric load forecasting

Prediction of Electrical Energy Consumption Using LSTM Algorithm with Teacher Forcing Technique

Adversarial Examples in Deep Learning for Multivariate Time Series Regression

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