Fast and Accurate Short-Term Load Forecasting with a Hybrid Model

Shin, Sang Mun; Rasheed, Asad; Kil-Heum, Park; Veluvolu, Kalyana C.

doi:10.3390/electronics13061079

Cited by 3 publications

(1 citation statement)

References 45 publications

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“…Short-term load forecasting (STLF) is useful for utilities in predicting energy demand during peak hours and preparing for potential supply shortages. Additionally, it assists electricity market participants in managing energy trading activities and mitigating risks arising from sudden fluctuations in demand or supply [4]. It was observed that 80% of the electrical energy demand forecasting concerned STLF, while MTLF and LTLF accounted for 15% and 5%, respectively.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

A Deep Learning Approach for Short-Term Electricity Demand Forecasting: Analysis of Thailand Data

Shiwakoti,

Charoenlarpnopparut,

Chapagain

2024

Applied Sciences

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Accurate electricity demand forecasting serves as a vital planning tool, enhancing the reliability of management decisions. Apart from that, achieving these aims, particularly in managing peak demand, faces challenges due to the industry’s volatility and the ongoing increase in residential energy use. Our research suggests that employing deep learning algorithms, such as recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent units (GRU), holds promise for the accurate forecasting of electrical energy demand in time series data. This paper presents the construction and testing of three deep learning models across three separate scenarios. Scenario 1 involves utilizing data from all-day demand. In Scenario 2, only weekday data are considered. Scenario 3 uses data from non-working days (Saturdays, Sundays, and holidays). The models underwent training and testing across a wide range of alternative hyperparameters to determine the optimal configuration. The proposed model’s validation involved utilizing a dataset comprising half-hourly electrical energy demand data spanning seven years from the Electricity Generating Authority of Thailand (EGAT). In terms of model performance, we determined that the RNN-GRU model performed better when the dataset was substantial, especially in scenarios 1 and 2. On the other hand, the RNN-LSTM model is excellent in Scenario 3. Specifically, the RNN-GRU model achieved an MAE (mean absolute error) of 214.79 MW and an MAPE (mean absolute percentage error) of 2.08% for Scenario 1, and an MAE of 181.63 MW and MAPE of 1.89% for Scenario 2. Conversely, the RNN-LSTM model obtained an MAE of 226.76 MW and an MAPE of 2.13% for Scenario 3. Furthermore, given the expanded dataset in Scenario 3, we can anticipate even higher precision in the results.

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Section: Introduction 1backgroundmentioning

confidence: 99%

A Deep Learning Approach for Short-Term Electricity Demand Forecasting: Analysis of Thailand Data

Shiwakoti,

Charoenlarpnopparut,

Chapagain

2024

Applied Sciences

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Enhancing Short-Term Load Forecasting Accuracy in High-Volatility Regions Using LSTM-SCN Hybrid Models

Tang,

Hu,

Yang

et al. 2024

Applied Sciences

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Short-Term Load Forecasting (STLF) is essential for the efficient management of power systems, as it improves forecasting accuracy while optimizing power scheduling efficiency. Despite significant recent advancements in STLF models, forecasting accuracy in high-volatility regions remains a key challenge. To address this issue, this paper introduces a hybrid load forecasting model that integrates the Long Short-Term Memory Network (LSTM) with the Stochastic Configuration Network (SCN). We first verify the Universal Approximation Property of SCN through experiments on two regression datasets. Subsequently, we reconstruct the features and input them into the LSTM for feature extraction. These extracted feature vectors are then used as inputs for SCN-based STLF. Finally, we evaluate the performance of the LSTM-SCN model against other baseline models using the Australian Electricity Load dataset. We also select five high-volatility regions in the test set to validate the LSTM-SCN model’s advantages in such scenarios. The results show that the LSTM-SCN model achieved an RMSE of 56.970, MAE of 43.033, and MAPE of 0.492% on the test set. Compared to the next best model, the LSTM-SCN model reduced errors by 6.016, 8.846, and 0.053% for RMSE, MAE, and MAPE, respectively. Additionally, the model consistently outperformed across all five high-volatility regions analyzed. These findings highlight its contribution to improved power system management, particularly in challenging high-volatility scenarios.

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Fusion of Hierarchical Optimization Models for Accurate Power Load Prediction

Wan,

Wang,

Zhang

et al. 2024

Sustainability

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Accurate power load forecasting is critical to achieving the sustainability of energy management systems. However, conventional prediction methods suffer from low precision and stability because of crude modules for predicting short-term and medium-term loads. To solve such a problem, a Combined Modeling Power Load-Forecasting (CMPLF) method is proposed in this work. The CMPLF comprises two modules to deal with short-term and medium-term load forecasting, respectively. Each module consists of four essential parts including initial forecasting, decomposition and denoising, nonlinear optimization, and evaluation. Especially, to break through bottlenecks in hierarchical model optimization, we effectively fuse the Nonlinear Autoregressive model with Exogenous Inputs (NARX) and Long-Short Term Memory (LSTM) networks into the Autoregressive Integrated Moving Average (ARIMA) model. The experiment results based on real-world datasets from Queensland and China mainland show that our CMPLF has significant performance superiority compared with the state-of-the-art (SOTA) methods. CMPLF achieves a goodness-of-fit value of 97.174% in short-term load prediction and 97.162% in medium-term prediction. Our approach will be of great significance in promoting the sustainable development of smart cities.

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Fast and Accurate Short-Term Load Forecasting with a Hybrid Model

Cited by 3 publications

References 45 publications

A Deep Learning Approach for Short-Term Electricity Demand Forecasting: Analysis of Thailand Data

A Deep Learning Approach for Short-Term Electricity Demand Forecasting: Analysis of Thailand Data

Enhancing Short-Term Load Forecasting Accuracy in High-Volatility Regions Using LSTM-SCN Hybrid Models

Fusion of Hierarchical Optimization Models for Accurate Power Load Prediction

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