Short-term electric power load forecasting using random forest and gated recurrent unit

Veeramsetty, Venkataramana; Reddy, K Rajeshwar; Santhosh, M; Mohnot, Arjun; Singal, Gaurav

doi:10.1007/s00202-021-01376-5

Cited by 45 publications

(20 citation statements)

References 36 publications

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“…The incentive received by the user in the current period is related to the previous historical power consumption status. When the user load demand is high, the demand response generated by the electricity price incentive has a great regulating effect on the transferable load [6]. When the proportion of electricity consumption expenditure is high, the possibility of adjustment elasticity caused by the electricity price incentive is higher than that in the scenario with a low proportion of expenditure.…”

Section: Analysis Of Power Consumption Behavior Considering Demand Si...mentioning

confidence: 99%

Research on Identification and Regulation of Power Load Characteristics Considering Demand Side Response

Yang

Deng

Liu

et al. 2022

J. Phys.: Conf. Ser.

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Due to the uncertainty of distributed generation output, it will cause some interference with the identification and regulation of load characteristics on the demand side, which is not conducive to improving the net load characteristics. To solve this problem, the identification and regulation methods of user power load characteristics considering demand side response are studied. When the user load demand is high, the demand response generated by the electricity price incentive has a greater regulating effect on the transferable load. According to the transient and steady-state characteristics of the load, the real-time data of electrical equipment are identified, and the composition and change of the user’s electrical load characteristics are obtained. At the beginning of the regulation period, the power enterprise receives the load characteristics on the demand side according to the user demand, determines the charging state and operation time of the power grid, and implements the operation of power consumption, idling, or discharging to minimize the cost. The test results show that this design method can accurately identify the load characteristics, and has a good effect in transferring peak load and alleviating peak valley difference, which provides a basis for users’ power use decisions.

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Section: Analysis Of Power Consumption Behavior Considering Demand Si...mentioning

confidence: 99%

Research on Identification and Regulation of Power Load Characteristics Considering Demand Side Response

Yang

Deng

Liu

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…The complete algorithm to train the RBFNN model using the stochastic gradient descent optimizer [43] is presented in Algorithm 1. The performance of the RBFNN is evaluated in terms of mean square error [44][45][46][47][48], as shown in Equation (4).…”

Section: Radial Basis Function Neural Network (Rbfnn)mentioning

confidence: 99%

Weather Forecasting Using Radial Basis Function Neural Network in Warangal, India

Veeramsetty¹,

Kiran²,

Sushma³

et al. 2023

Urban Science

Self Cite

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Weather forecasting is an essential task in any region of the world for proper planning of various sectors that are affected by climate change. In Warangal, most sectors, such as agriculture and electricity, are mainly influenced by climate conditions. In this study, weather (WX) in the Warangal region was forecast in terms of temperature and humidity. A radial basis function neural network was used in this study to forecast humidity and temperature. Humidity and temperature data were collected for the period of January 2021 to December 2021. Based on the simulation results, it is observed that the radial basis function neural network model performs better than other machine learning models when forecasting temperature and humidity.

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“…It's worth noting that the shortcoming of gradient vanishing during RNN training weakens the capability to capture long-term information. To address this issue, long short-term memory (LSTM) network, echo state network (ESN) and gated recurrent unit (GRU) network as the optimized versions of traditional RNN are employed to perform load forecasting in [19][20][21], respectively. In particular, GRU simplifies three unit functions of LSTM and ESN into two unit functions to fully decrease the number of model parameters, reducing the risk of model overfitting.…”

Section: Introductionmentioning

confidence: 99%

A hybrid deep learning model for short‐term load forecasting of distribution networks integrating the channel attention mechanism

Qin,

Gao,

Ding

et al. 2024

IET Generation Trans & Dist

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Optimizing short‐term load forecasting performance is a challenge due to the randomness of nonlinear power load and variability of system operation mode. The existing methods generally ignore how to reasonably and effectively combine the complementary advantages among them and fail to capture enough internal information from load data, resulting in accuracy reduction. To achieve accurate and efficient short‐term load forecasting, an integral implementation framework is proposed based on convolutional neural network (CNN), gated recurrent unit (GRU) and channel attention mechanism. CNN and GRU are first combined to fully extract the highly complicated dynamic characteristics and learn time compliance relationships of load sequence. Based on CNN‐GRU network, the channel attention mechanism is introduced to further reduce the loss of historical information and enhance the impact of important characteristics. Then, the overall framework of short‐term load forecasting based on CNN‐GRU‐Attention network is proposed, and the coupling relationship between each stage is revealed. Finally, the developed framework is implemented on realistic load dataset of distribution networks, and the experimental results verify the effectiveness of the proposed method. Compared with the state‐of‐the‐art models, the CNN‐GRU‐Attention model outperforms in different evaluation metrics.

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Short-term electric power load forecasting using random forest and gated recurrent unit

Cited by 45 publications

References 36 publications

Research on Identification and Regulation of Power Load Characteristics Considering Demand Side Response

Research on Identification and Regulation of Power Load Characteristics Considering Demand Side Response

Weather Forecasting Using Radial Basis Function Neural Network in Warangal, India

A hybrid deep learning model for short‐term load forecasting of distribution networks integrating the channel attention mechanism

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