Very‐short‐term load forecasting based on empirical mode decomposition and deep neural network

2019 ◽  
Vol 15 (2) ◽  
pp. 252-258 ◽  
Author(s):  
Li‐Min Cheng ◽  
Yu‐Qing Bao ◽  
Lai Tang ◽  
Hui‐Fang Di
Keyword(s):  
Neural Network ◽  
Empirical Mode Decomposition ◽  
Deep Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
Mode Decomposition ◽  
Short Term Load Forecasting

scholarly journals Short-Term Load Forecasting Method based on Empirical Wavelet Decomposition and BLSTM Neural Networks

2021 ◽  
Author(s):  
Xiao-Yu Zhang ◽  
Stefanie Kuenzel ◽  
Nicolo Colombo ◽  
Chris Watkins
Keyword(s):  
Neural Network ◽  
Frequency Domain ◽  
Time Domain ◽  
Deep Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
Forecasting Models ◽  
Load Demand ◽  
Proposed Model ◽  
Short Term Load Forecasting

Accurate short-term load forecasting is essential to the modern power system and smart grids; the utility can better implement demand-side management and operate the power system stable with a reliable forecasting system. The load demand contains a variety of different load components, and different loads operate with different frequencies. Conventional load forecasting models (linear regression (LR), Auto-Regressive Integrated Moving Average (ARIMA), deep neural network, etc.) ignore frequency domain and can only use time-domain load demand as inputs. To make full use of both time domain and frequency domain features of the load demand, a hybrid component decomposition and deep neural network load forecasting model is proposed in this paper. The proposed model first filters noises via wavelet-based denoising technique, then decomposes the original load demand into several sublayers to show the frequency features while the time domain information is preserved as well. Then bidirectional LSTM model is trained for each sub-layer independently. To better tunning the hyperparameters, a Bayesian hyperparameter optimization algorithm is adopted in this paper. Three case studies are designed to evaluate the performance of the proposed model. From the results, it is found that the proposed model improves RMSE by 66.59% and 84.06%, comparing to other load forecasting models.<br>

Analysis and evaluation of two short-term load forecasting techniques

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Saroj Kumar Panda ◽  
Papia Ray
Keyword(s):  
Neural Network ◽  
Power System ◽  
Deep Neural Network ◽  
Load Forecasting ◽  
Percentage Error ◽  
Correlation Technique ◽  
Short Term ◽  
Efficient Operation ◽  
Decision Mechanism ◽  
Short Term Load Forecasting

Abstract Short-term load forecasting (STLF) is very important for an efficient operation of the power system because the exact and stable load forecasting brings good results to the power system. This manuscript presents the application of two new models in STLF i.e. Cross multi-models and second decision mechanism and Residential load forecasting in smart grid using deep neural network models. In the cross multi-model and second decision mechanism method, the horizontal and longitudinal load characteristics are useful for the construction of the model with the calculation of the total load. The dataset for this model is considered from Maine in New England, Singapore, and New South Wales of Australia. While, In the residential load forecasting method, the Spatio-temporal correlation technique is used for the construction of the iterative ResBlock and deep neural network which helps to give the characteristics of residential load with the use of a publicly available Redd dataset. The performances of the proposed models are calculated by the Root Mean Square Error, Mean Absolute Error, and Mean Absolute Percentage Error. From the simulation results, it is concluded that the performance of cross multi-model and second decision mechanism is good as compare to the residential load forecasting.

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