Electricity Demand Forecasting for Smart Grid Based on Deep Learning Approach

Nguyen, Vanthuan; Duong, Minh-Thien; Le, My-Ha

doi:10.1109/gtsd50082.2020.9303164

Cited by 8 publications

(2 citation statements)

References 13 publications

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“…The study conducted has revealed that the most widely used Deep Learning models in the energy domain for demand forecasting purposes are CNNs, RNNs, LSTM, DQNs, and CRBM and a variation of any of them, a combination of two or more of them, or the combination of any of them with other techniques. Notable are CNN and its variations such as Pyramid-CNN [ 82 , 85 , 88 , 90 , 91 , 94 , 95 , 101 , 106 , 107 , 109 , 115 , 118 , 119 , 123 ], LSTM and its variations such as B-LSTM [ 80 , 82 , 86 , 87 , 88 , 91 , 93 , 94 , 95 , 99 , 100 , 103 , 104 , 106 , 107 , 109 , 110 , 111 , 112 , 113 , 118 , 119 , 122 ], and a combination of both [ 82 , 88 , 91 , 94 , 95 , 106 , 107 , 109 , 118 , 119 ]. Real testbeds with high-quality data are not common, but are necessary to determine the performance of Deep Leaning models.…”

Section: Discussionmentioning

confidence: 99%

An Insight of Deep Learning Based Demand Forecasting in Smart Grids

Aguiar-Pérez

Pérez-Juárez

2023

Sensors

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Smart grids are able to forecast customers’ consumption patterns, i.e., their energy demand, and consequently electricity can be transmitted after taking into account the expected demand. To face today’s demand forecasting challenges, where the data generated by smart grids is huge, modern data-driven techniques need to be used. In this scenario, Deep Learning models are a good alternative to learn patterns from customer data and then forecast demand for different forecasting horizons. Among the commonly used Artificial Neural Networks, Long Short-Term Memory networks—based on Recurrent Neural Networks—are playing a prominent role. This paper provides an insight into the importance of the demand forecasting issue, and other related factors, in the context of smart grids, and collects some experiences of the use of Deep Learning techniques, for demand forecasting purposes. To have an efficient power system, a balance between supply and demand is necessary. Therefore, industry stakeholders and researchers should make a special effort in load forecasting, especially in the short term, which is critical for demand response.

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Section: Discussionmentioning

confidence: 99%

An Insight of Deep Learning Based Demand Forecasting in Smart Grids

Aguiar-Pérez

Pérez-Juárez

2023

Sensors

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“…RNN count the present input and the output acquired from the last input while it makes logical decision. RNNs can utilize the internal state (memory) to evaluate input variables, which is different from feedforward neural network [31,32]. In recurrent neural network, all of the inputs are connected with one another, which distinguishes it from the other neural networks.…”

Section: Gated Recurrent Unit (Gru)mentioning

confidence: 99%

Optimized Gated Recurrent Unit for Mid-Term Electricity Price Forecasting

Iqbal¹,

Mokhlis²,

Khairuddin³

et al. 2022

Computer Systems Science and Engineering

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Electricity price forecasting (EPF) is important for energy system operations and management which include strategic bidding, generation scheduling, optimum storage reserves scheduling and systems analysis. Moreover, accurate EPF is crucial for the purpose of bidding strategies and minimizing the risk for market participants in the competitive electricity market. Nevertheless, accurate time-series prediction of electricity price is very challenging due to complex nonlinearity in the trend of electricity price. This work proposes a mid-term forecasting model based on the demand and price data, renewable and non-renewable energy supplies, the seasonality and peak and off-peak hours of working and nonworking days. An optimized Gated Recurrent Unit (GRU) which incorporates Bagged Regression Tree (BTE) is developed in the Recurrent Neural Network (RNN) architecture for the mid-term EPF. Tanh layer is employed to optimize the hyperparameters of the heterogeneous GRU with the aim to improve the model's performance, error reduction and predict the spikes. In this work, the proposed framework is assessed using electricity market data of five major economical states in Australia by using electricity market data from August 2020 to May 2021. The results showed significant improvement when adopting the proposed prediction framework compared to previous works in forecasting the electricity price.

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Electricity Demand Forecasting Using ML

Daniel Raj,

et al. 2023

2023 3rd International Conference on Pervasive Computing and Social Networking (ICPCSN)

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Electricity Demand Forecasting for Smart Grid Based on Deep Learning Approach

Cited by 8 publications

References 13 publications

An Insight of Deep Learning Based Demand Forecasting in Smart Grids

An Insight of Deep Learning Based Demand Forecasting in Smart Grids

Optimized Gated Recurrent Unit for Mid-Term Electricity Price Forecasting

Electricity Demand Forecasting Using ML

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