Short-Term Load Forecasting and Associated Weather Variables Prediction Using ResNet-LSTM Based Deep Learning

Chen, Xinfang; Chen, Weiran; Dinavahi, Venkata; Liu, Yiqing; Feng, Jie

doi:10.1109/access.2023.3236663

Cited by 43 publications

(17 citation statements)

References 56 publications

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“…In this section, the recent state-of-the-art (SOTA) models ( Ryu, Noh & Kim, 2016 ; Kong et al, 2017 ; Marino, Amarasinghe & Manic, 2016 ; Rafi, Deeba & Hossain, 2021 ; Alhussein, Aurangzeb & Haider, 2020 ; Ullah et al, 2019 ; Ijaz et al, 2022 ; Chen et al, 2023 ; Hussain et al, 2022 ; Shao & Kim, 2020 ) are selected from the literature and compared their results with the proposed model. All these models are reproduced by one-to-one correspondence with the source article.…”

Section: Resultsmentioning

confidence: 99%

“…Similarly, in Sajjad et al (2020) a stack of two layers of CNN is followed by two layers of GRU while in Ullah et al (2019) , a two-layer CNN is followed by Multi-layer Bi-Directional LSTM (M-BDLSTM) layer. Similarly, Chen et al (2023) , Hussain et al (2022) , used hybrid models composed of CNN and RNN variants for load forecasting. However, in Chen et al (2023) , the authors used CNN followed by four ResNet modules.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, Chen et al (2023) , Hussain et al (2022) , used hybrid models composed of CNN and RNN variants for load forecasting. However, in Chen et al (2023) , the authors used CNN followed by four ResNet modules. These existing hybrid models outperform the previous state-of-the-art models.…”

Section: Introductionmentioning

confidence: 99%

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Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution

Ullah,

Muhammad Hasanat,

Aurangzeb

et al. 2023

PeerJ Computer Science

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Precise short-term load forecasting (STLF) plays a crucial role in the smooth operation of power systems, future capacity planning, unit commitment, and demand response. However, due to its non-stationary and its dependency on multiple cyclic and non-cyclic calendric features and non-linear highly correlated metrological features, an accurate load forecasting with already existing techniques is challenging. To overcome this challenge, a novel hybrid technique based on long short-term memory (LSTM) and a modified split-convolution (SC) neural network (LSTM-SC) is proposed for single-step and multi-step STLF. The concatenating order of LSTM and SC in the proposed hybrid network provides an excellent capability of extraction of sequence-dependent features and other hierarchical spatial features. The model is evaluated by the Pakistan National Grid load dataset recorded by the National Transmission and Dispatch Company (NTDC). The load data is pre-processed and multiple other correlated features are incorporated into the data for performance enhancement. For generalization capability, the performance of LSTM-SC is evaluated on publicly available datasets of American Electric Power (AEP) and Independent System Operator New England (ISO-NE). The effect of temperature, a highly correlated input feature, on load forecasting is investigated either by removing the temperature or adding a Gaussian random noise into it. The performance evaluation in terms of RMSE, MAE, and MAPE of the proposed model on the NTDC dataset are 500.98, 372.62, and 3.72% for multi-step while 322.90, 244.22, and 2.38% for single-step load forecasting. The result shows that the proposed method has less forecasting error, strong generalization capability, and satisfactory performance on multi-horizon.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution

Ullah,

Muhammad Hasanat,

Aurangzeb

et al. 2023

PeerJ Computer Science

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“…The forget gate can selectively forget the last few sets of states and correct the parameters [27], which determines what information the LSTM unit needs to forget from the cell state C t and what information it needs to retain. The forget gate checks the output vector C t−1 from the previous LSTM unit, combines the parameter h t−1 passed from the previous time step with the input value X t of the current time step, and outputs the number from 0 to 1 [28] via the activation function σ (i.e., sigmoid function), where 0 means forget it completely, and 1 means keep it completely.…”

Section: Forget Gatementioning

confidence: 99%

Long Short-Term Memory Network for Predicting Wind-Induced Vibration Response of Lightning Rod Structures

et al. 2023

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Lightning rod structures are susceptible to wind loads due to their high slenderness ratio, high flexibility, and light weight. The wind-induced dynamic response of a lightning rod is critical for structural safety and reliability. The traditional methods for this response, including observation and simulation, focus on structural health monitoring (SHM), wind tunnel tests (WTTs), or fluid–structure interaction (FSI) simulations. However, all these approaches require considerable financial or computational investment. Additionally, problems such as data loss or data anomalies in the sensor monitoring process often occur during SHM or WTTs. This paper proposes an algorithm based on a long short-term memory (LSTM) network to predict the wind-induced dynamic response and to solve the problem of data link fracture caused by abnormal sensor data transmission or wind-induced damage to lightning rod structures under different wind speeds. The effectiveness and applicability of the proposed framework are demonstrated using actual monitoring data. Root-mean-squared error (RMSE), determination of coefficient (R2), variance accounted for (VAF), and the refined Willmott index (RWI) are employed as performance assessment indices for the proposed network model. At the same time, the random forest algorithm is adopted to analyze the correlation between the data of the different measurement points on the lightning rod structure. The results show that the LSTM method proposed in this paper has a high accuracy for the prediction of “missing” strain data during lightning rod strain monitoring under wind speeds of 15.81~31.62 m/s. Even under the extreme wind speed of 31.62 m/s, the values of RMSE, MAE, R2, RWI and VAF are 0.24053, 0.18213, 0.94539, 0.88172 and 0.94444, respectively, which are within the acceptable range. Using the data feature importance analysis function, it is found that the predicted strain data of the measurement point on the top part of the lightning rod structure are closely related to the test strain data of the two adjacent sections of the structure, and the effect of the test strain data of the measurement points that are far from the predicted measurement point can be ignored.

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“…Similarly, in [7], the combination of genetic algorithm (GA) and bidirectional gated recurrent unit (Bi-GRU) was proposed for STLF in Bangladesh, outperforming other techniques with only a minimal decrease of 18.13% and 19.82% in RMSE and MAPE, respectively. Another hybrid method, proposed by Chen et al [31], combines Residual Neural Network (ResNet) and LSTM to accurately forecast short-term load for Queensland, Australia. However, the proposed model architecture is more computationally expensive and requires a larger training and inference period.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Short-Term Load Forecasting for Multiple Locations: An Integrated Approach

Baul,

Sarker,

Sikder

et al. 2024

BDCC

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Short-term load forecasting (STLF) plays a crucial role in the planning, management, and stability of a country’s power system operation. In this study, we have developed a novel approach that can simultaneously predict the load demand of different regions in Bangladesh. When making predictions for loads from multiple locations simultaneously, the overall accuracy of the forecast can be improved by incorporating features from the various areas while reducing the complexity of using multiple models. Accurate and timely load predictions for specific regions with distinct demographics and economic characteristics can assist transmission and distribution companies in properly allocating their resources. Bangladesh, being a relatively small country, is divided into nine distinct power zones for electricity transmission across the nation. In this study, we have proposed a hybrid model, combining the Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU), designed to forecast load demand seven days ahead for each of the nine power zones simultaneously. For our study, nine years of data from a historical electricity demand dataset (from January 2014 to April 2023) are collected from the Power Grid Company of Bangladesh (PGCB) website. Considering the nonstationary characteristics of the dataset, the Interquartile Range (IQR) method and load averaging are employed to deal effectively with the outliers. Then, for more granularity, this data set has been augmented with interpolation at every 1 h interval. The proposed CNN-GRU model, trained on this augmented and refined dataset, is evaluated against established algorithms in the literature, including Long Short-Term Memory Networks (LSTM), GRU, CNN-LSTM, CNN-GRU, and Transformer-based algorithms. Compared to other approaches, the proposed technique demonstrated superior forecasting accuracy in terms of mean absolute performance error (MAPE) and root mean squared error (RMSE). The dataset and the source code are openly accessible to motivate further research.

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Short-Term Load Forecasting and Associated Weather Variables Prediction Using ResNet-LSTM Based Deep Learning

Cited by 43 publications

References 56 publications

Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution

Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution

Long Short-Term Memory Network for Predicting Wind-Induced Vibration Response of Lightning Rod Structures

Data-Driven Short-Term Load Forecasting for Multiple Locations: An Integrated Approach

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