Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network

Kong, Weicong; Zhang, Dong; Jia, Youwei; Hill, David J.; Xu, Yan; Zhang, Yuan

doi:10.1109/tsg.2017.2753802

Cited by 1,800 publications

(1,043 citation statements)

References 27 publications

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“…These five methods were chosen based on empirical results on the holdout validation data. In addition we considered Holt-Winters (Hyndman et al, 2002), BATS (Livera et al, 2011), LSTM networks (Kong et al, 2019) and a combination of ARIMA and a neural network (Zhang, 2003). On each time series, we trained each of the chosen methods to obtain five models.…”

Section: Ensembling Modelmentioning

confidence: 99%

Correlated daily time series and forecasting in the M4 competition

Ingel

Shahroudi

Kängsepp

et al. 2020

International Journal of Forecasting

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We participated in the M4 competition for time series forecasting and describe here our methods for forecasting daily time series. We used an ensemble of five statistical forecasting methods and a method that we refer to as the correlator. Our retrospective analysis using the ground truth values published by the M4 organisers after the competition demonstrates that the correlator was responsible for most of our gains over the naive constant forecasting method. We identify data leakage as one reason for its success, partly due to test data selected from different time intervals, and partly due to quality issues in the original time series. We suggest that future forecasting competitions should provide actual dates for the time series so that some of those leakages could be avoided by the participants.

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Section: Ensembling Modelmentioning

confidence: 99%

Correlated daily time series and forecasting in the M4 competition

Ingel

Shahroudi

Kängsepp

et al. 2020

International Journal of Forecasting

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“…We can conclude that GRU neural networks do better in both convergence speed and training time, which depends on the improved single structure of GRU units. We also performed the experiments to compare with current methods such as back-propagation neural networks (BPNNs) [7,8], stacked autoencoders (SAEs) [17], RNNs [24,25], and LSTM [29][30][31]. Their parameters and structures are set as described in Section 3.2.…”

Section: Comparison Of Results Of Proposed Methodsmentioning

confidence: 99%

Short-Term Load Forecasting with Multi-Source Data Using Gated Recurrent Unit Neural Networks

et al. 2018

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Short-term load forecasting is an important task for the planning and reliable operation of power grids. High-accuracy forecasting for individual customers helps to make arrangements for generation and reduce electricity costs. Artificial intelligent methods have been applied to short-term load forecasting in past research, but most did not consider electricity use characteristics, efficiency, and more influential factors. In this paper, a method for short-term load forecasting with multi-source data using gated recurrent unit neural networks is proposed. The load data of customers are preprocessed by clustering to reduce the interference of electricity use characteristics. The environmental factors including date, weather and temperature are quantified to extend the input of the whole network so that multi-source information is considered. Gated recurrent unit neural networks are used for extracting temporal features with simpler architecture and less convergence time in the hidden layers. The detailed results of the real-world experiments are shown by the forecasting curve and mean absolute percentage error to prove the availability and superiority of the proposed method compared to the current forecasting methods.

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“…Appliance-level load curves are valuable in demand prediction and scheduling. Prior studies [2][3][4] pointed out that the analysis and sound understanding of demand profile on smaller consumer level could provide reasonably accurate information for predicting peak and average demand and demand shifting. Besides, NILM can also increase the penetration of renewable sources by maintaining the cost and revenue balance in micro-grid [5].…”

Section: Introductionmentioning

confidence: 99%

A Supervised Event-Based Non-Intrusive Load Monitoring for Non-Linear Appliances

2018

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Smart meters generate a massive volume of energy consumption data which can be analyzed to recover some interesting and beneficial information. Non-intrusive load monitoring (NILM) is one important application fostered by the mass deployment of smart meters. This paper presents a supervised event-based NILM approach for non-linear appliance activities identification. Firstly, the additive properties (stating that, when a certain amount of specific appliances' feature is added to their belonging network, an equal amount of change in the network's feature can be observed) of three features (harmonic feature, voltage-current trajectory feature, and active-reactive-distortion (PQD) power curve features) were investigated through experiments. The results verify the good additive property for the harmonic features and Voltage-Current (U-I) trajectory features. In contrast, PQD power curve features have a poor additive property. Secondly, based on the verified additive property of harmonic current features and the representation of waveforms, a harmonic current features based approach is proposed for NILM, which includes two main processes: event detection and event classification. For event detection, a novel model is proposed based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Compared to other event detectors, the proposed event detector not only can detect both event timestamp and two adjacent steady states but also shows high detection accuracy over public dataset with F1-score up to 98.99%. Multi-layer perceptron (MLP) classifiers are then built for multi-class event classification using the harmonic current features and are trained using the data collected from the laboratory and the public dataset. The results show that the MLP classifiers have a good performance in classifying non-linear loads. Finally, the proposed harmonic current features based approach is tested in the laboratory through experiments, in which multiple on-off events of multiple appliances occur. The research indicates that clustering-based event detection algorithms are promising for future works in event-based NILM. Harmonic current features have perfect additive property, and MLP classifier using harmonic current features can accurately identify typical non-linear and resistive loads, which could be integrated with other approaches in the future.

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Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network

Cited by 1,800 publications

References 27 publications

Correlated daily time series and forecasting in the M4 competition

Correlated daily time series and forecasting in the M4 competition

Short-Term Load Forecasting with Multi-Source Data Using Gated Recurrent Unit Neural Networks

A Supervised Event-Based Non-Intrusive Load Monitoring for Non-Linear Appliances

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