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

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Abstract

The main purpose of this paper is to develop an efficient machine learning model to estimate the electric power load. The developed machine learning model can be used by electric power utilities for proper operation and maintenance of grid and also to trade electricity effectively in energy market. This paper proposes a machine learning model using gated recurrent unit (GRU) and random forest (RF). GRU has been employed to predict the electric power load, whereas RF has been used to reduce the input dimensions of the model. GRU has been estimating the load with good accuracy. RF reduces the input dimensions of the GRU that leads lightweight GRU model. The main benefits of the lightweight GRU models are less computation time and memory space. However, lightweight GRU models will loss small amount of accuracy comparing to the original GRU model. GRU along with RF has been used for the first for short load forecasting. All the machine learning model’s performance has been observed in stochastic environment. Impact of weekends on load forecasting also observed by considering the last 3-week load data.

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Abbreviations

\(a_{t-1}\) :

Hidden neuron previous activation state

\(a_{t}\) :

Hidden neuron current activation state

\(b_{c}\) :

Bias parameter to compute cell state

\(b_{r}\) :

Bias parameter to compute relevance gate

\(b_{u}\) :

Bias parameter to compute update gate

\(b_{y}\) :

Bias parameter for output layer

\(c^{t}\) :

State of memory cell

\(W_{c}\) :

Weight matrix to compute cell state

\(W_{r}\) :

Weight matrix to compute relevance gate

\(W_{u}\) :

Weight matrix to compute update gate

\(W_{y}\) :

Weight matrix to compute output

\(x_{t}\) :

Input feature at time stamp ‘t’

\(Y_{i}^\mathrm{pred}\) :

Predicted load with ith sample

\(Y_{i}^\mathrm{true}\) :

Actual load from ith sample

L(T):

Load at Tth hour

L(T-1):

Load at 1 h before from the time of prediction

L(T-168):

Load at 1 week before from the time of prediction

L(T-2):

Load at 2 h before from the time of prediction

L(T-24):

Load at 1 day before from the time of prediction

L(T-3):

Load at 3 h before from time of prediction

L(T-336):

Load at 2 weeks before from the time of prediction

L(T-48):

Load at 2 days before from the time of prediction

L(T-504):

Load at 3 weeks before from time of prediction

L(T-72):

Load at 3 days before from time of prediction

MAE:

Mean absolute error

MSE:

Mean square error

RMSE:

Root mean square error

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Acknowledgements

We thank S R engineering College, Warangal, for supporting us during this work. We thank 33/11-kV substation near Kakatiya University in Warangal for providing the historical load data.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Center for Artificial Intelligence and Deep Learning, S R Engineering College, Warangal, Telangana State, India

    Venkataramana Veeramsetty

  2. Department of Electrical and Electronics Engineering, S R University, Warangal, Telangana State, India

    K. Rajeshwar Reddy

  3. Department of Electrical and Electronics Engineering, Kakatiya Institute of Technology and Science, Warangal, Telangana State, India

    M. Santhosh

  4. Department of Computer Science Engineering, Bennett University, Noida, India

    Arjun Mohnot & Gaurav Singal

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  1. Venkataramana Veeramsetty
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Correspondence to Venkataramana Veeramsetty.

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Veeramsetty, V., Reddy, K.R., Santhosh, M. et al. Short-term electric power load forecasting using random forest and gated recurrent unit. Electr Eng 104, 307–329 (2022). https://doi.org/10.1007/s00202-021-01376-5

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  • DOI: https://doi.org/10.1007/s00202-021-01376-5

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