An Artificial Intelligence Based Day Lag Technique for Day Ahead Short Term Load Forecasting

Inteha, Azfar; Masood, Nahid‐Al; Deeba, Shohana Rahman

doi:10.1109/tensymp50017.2020.9230805

Cited by 3 publications

(3 citation statements)

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“…In the proposed model architecture, a 1D-CNN is employed to extract features from the historical load dataset, where the number of filters and kernel size are crucial parameters. Various experiments were conducted with different numbers of filters (32,64,128) and different kernel sizes (3,5,7). It was observed that 128 filters with a window size of 3 generate the lowest validation loss.…”

Section: Model Architecturementioning

confidence: 99%

“…The second category, known as midterm load forecasting, anticipates energy demand from one week to several months and occasionally extends to a year [6]. Long-term load forecasting focuses on predicting energy consumption over a timeframe exceeding a year [7]. While short-and mid-term forecasting are instrumental for efficient system operation management, long-term electricity demand forecasting facilitates the development of power system infrastructure [8].…”

Section: Introductionmentioning

confidence: 99%

“…However, the study did not investigate the impact of additional features, such as temperature and weekday, on the predictive performance. 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.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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