scholarly journals A Hybrid System Based on LSTM for Short-Term Power Load Forecasting

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6241 ◽  
Author(s):  
Yu Jin ◽  
Honggang Guo ◽  
Jianzhou Wang ◽  
Aiyi Song
Keyword(s):  
Optimization Technique ◽  
Load Forecasting ◽  
South Australia ◽  
Vital Role ◽  
Prediction Performance ◽  
Load Prediction ◽  
Short Term ◽  
South Wales ◽  
Power Load ◽  
Data Pretreatment

As the basic guarantee for the reliability and economic operations of state grid corporations, power load prediction plays a vital role in power system management. To achieve the highest possible prediction accuracy, many scholars have been committed to building reliable load forecasting models. However, most studies ignore the necessity and importance of data preprocessing strategies, which may lead to poor prediction performance. Thus, to overcome the limitations in previous studies and further strengthen prediction performance, a novel short-term power load prediction system, VMD-BEGA-LSTM (VLG), integrating a data pretreatment strategy, advanced optimization technique, and deep learning structure, is developed in this paper. The prediction capability of the new system is evaluated through simulation experiments that employ the real power data of Queensland, New South Wales, and South Australia. The experimental results indicate that the developed system is significantly better than other comparative systems and shows excellent application potential.

Load Prediction of RBF Neural Network Considering Weather Factors

2013 ◽  
Vol 397-400 ◽  
pp. 1103-1106
Author(s):  
Ren Ran Wei ◽  
Zhen Zhu Wei ◽  
Hai Bo Yang ◽  
Jian Dong Jiang
Keyword(s):  
Neural Network ◽  
Neural Network Model ◽  
Prediction Accuracy ◽  
Rbf Neural Network ◽  
Load Forecasting ◽  
Weather Data ◽  
Load Prediction ◽  
Short Term ◽  
Weather Factors ◽  
Power Load

In order to improve the precision of the short-term load prediction, a new method based on radial basis function (RBF) neural network is proposed. The weather data of samples includes the temperature, humidity, date, type, etc., and is quantified according the relevance to load, and then forecasting the power load using RBF neural network model in a region, Actual example shows that this method improves the convergence speed and prediction accuracy of load forecasting.

scholarly journals Forecasting for Ultra-Short-Term Electric Power Load Based on Integrated Artificial Neural Networks

Symmetry ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1063 ◽  
Author(s):  
Horng-Lin Shieh ◽  
Fu-Hsien Chen
Keyword(s):  
Energy Efficiency ◽  
Electric Power ◽  
Production Efficiency ◽  
Load Forecasting ◽  
Short Term ◽  
Inference System ◽  
Power Load ◽  
Power Load Forecasting ◽  
New Energy ◽  
Electric Power Load

Energy efficiency and renewable energy are the two main research topics for sustainable energy. In the past ten years, countries around the world have invested a lot of manpower into new energy research. However, in addition to new energy development, energy efficiency technologies need to be emphasized to promote production efficiency and reduce environmental pollution. In order to improve power production efficiency, an integrated solution regarding the issue of electric power load forecasting was proposed in this study. The solution proposed was to, in combination with persistence and search algorithms, establish a new integrated ultra-short-term electric power load forecasting method based on the adaptive-network-based fuzzy inference system (ANFIS) and back-propagation neural network (BPN), which can be applied in forecasting electric power load in Taiwan. The research methodology used in this paper was mainly to acquire and process the all-day electric power load data of Taiwan Power and execute preliminary forecasting values of the electric power load by applying ANFIS, BPN and persistence. The preliminary forecasting values of the electric power load obtained therefrom were called suboptimal solutions and finally the optimal weighted value was determined by applying a search algorithm through integrating the above three methods by weighting. In this paper, the optimal electric power load value was forecasted based on the weighted value obtained therefrom. It was proven through experimental results that the solution proposed in this paper can be used to accurately forecast electric power load, with a minimal error.

scholarly journals Deep Neural Network and Long Short-Term Memory for Electric Power Load Forecasting

2020 ◽  
Vol 10 (18) ◽  
pp. 6489
Author(s):  
Namrye Son ◽  
Seunghak Yang ◽  
Jeongseung Na
Keyword(s):  
Power Consumption ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Power Demand ◽  
Short Term ◽  
Power Load ◽  
Short And Long Term ◽  
Long Short Term Memory ◽  
Electric Power Load

Forecasting domestic and foreign power demand is crucial for planning the operation and expansion of facilities. Power demand patterns are very complex owing to energy market deregulation. Therefore, developing an appropriate power forecasting model for an electrical grid is challenging. In particular, when consumers use power irregularly, the utility cannot accurately predict short- and long-term power consumption. Utilities that experience short- and long-term power demands cannot operate power supplies reliably; in worst-case scenarios, blackouts occur. Therefore, the utility must predict the power demands by analyzing the customers’ power consumption patterns for power supply stabilization. For this, a medium- and long-term power forecasting is proposed. The electricity demand forecast was divided into medium-term and long-term load forecast for customers with different power consumption patterns. Among various deep learning methods, deep neural networks (DNNs) and long short-term memory (LSTM) were employed for the time series prediction. The DNN and LSTM performances were compared to verify the proposed model. The two models were tested, and the results were examined with the accuracies of the six most commonly used evaluation measures in the medium- and long-term electric power load forecasting. The DNN outperformed the LSTM, regardless of the customer’s power pattern.

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