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Short-Term Load and Price Forecasting based on Improved Convolutional Neural Network

  • January 2020
DOI:10.1109/iCoMET48670.2020.9074080
  • Conference: 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)
Authors:
Beenish Khan at COMSATS University Islamabad
Beenish Khan
Rabiya Khalid at COMSATS University Islamabad
Rabiya Khalid
Muhammad Umar Javed at University of Wah
Muhammad Umar Javed
Sakeena Javaid at Institute of Space Technology KICSIT Campus
Sakeena Javaid
  • Institute of Space Technology KICSIT Campus
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Short-Term Load and Price Forecasting based on Improved Convolutional Neural Netwo
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... Assuming that all parameters of the DA-BiGRU model are θ, its loss function is shown in Equation (17). With the goal of minimizing this loss function, the final DA-BiGRU prediction model is obtained when the training is over. ...
... (5) Horizontal crossover: According to Equation (22), perform horizontal crossover on the parent population Φ to obtain the offspring population Φ HC , and use Equation (17) to calculate the fitness of each individual. If the fitness of individual θ k (k ∈ {1, 2, . . . ...
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Accurate short-term load forecasting is of great significance to the safe and stable operation of power systems and the development of the power market. Most existing studies apply deep learning models to make predictions considering only one feature or temporal relationship in load time series. Therefore, to obtain an accurate and reliable prediction result, a hybrid prediction model combining a dual-stage attention mechanism (DA), crisscross grey wolf optimizer (CS-GWO) and bidirectional gated recurrent unit (BiGRU) is proposed in this paper. DA is introduced on the input side of the model to improve the sensitivity of the model to key features and information at key time points simultaneously. CS-GWO is formed by combining the horizontal and vertical crossover operators, to enhance the global search ability and the diversity of the population of GWO. Meanwhile, BiGRU is optimized by CS-GWO to accelerate the convergence of the model. Finally, a collected load dataset, four evaluation metrics and parametric and non-parametric testing manners are used to evaluate the proposed CS-GWO-DA-BiGRU short-term load prediction model. The experimental results show that the RMSE, MAE and SMAPE are reduced respectively by 3.86%, 1.37% and 0.30% of those of the second-best performing CSO-DA-BiGRU model, which demonstrates that the proposed model can better fit the load data and achieve better prediction results.
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... Similarly, peak electricity demand can be observed in monthly Buddhists' holiday called "Poya day". The unique and unsteady peak demand pattern is shown because of the Sinhalese and Tamil cultural festival season called as "New Year Season" during April and Buddhists' religious activities for "Wesak Poya" in May [8]. It is necessary to clean and scale data to zero mean and unit variance under the preprocessing step, after collecting the data of highly correlated factors to the electricity demand forecasting [9]. ...
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