scholarly journals Short-Term Power Load Forecasting Under COVID-19 Based on Graph Representation Learning With Heterogeneous Features

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhuowei Yu ◽  
Jiajun Yang ◽  
Yufeng Wu ◽  
Yi Huang
Keyword(s):  
Time Moment ◽  
Load Forecasting ◽  
Representation Learning ◽  
Graph Representation ◽  
Smooth Transition ◽  
Short Term ◽  
Power Load ◽  
Heterogeneous Features ◽  
Short Term Load Forecasting ◽  
Power Load Forecasting

Since 2020, the COVID-19 has spread globally at an extremely rapid rate. The epidemic, vaccination, and quarantine policies have profoundly changed economic development and human activities worldwide. As many countries start to resume economic activities aiming at a “living with COVID” new normal, a short-term load forecasting technique incorporating the epidemic’s effects is of great significance to both power system operation and a smooth transition. In this context, this paper proposes a novel short-term load forecasting method under COVID-19 based on graph representation learning with heterogeneous features. Unlike existing methods that fit power load data to time series, this study encodes heterogeneous features relevant to electricity consumption and epidemic status into a load graph so that not only the features at each time moment but also the inherent correlations between the features can be exploited; Then, a residual graph convolutional network (ResGCN) is constructed to fit the non-linear mappings from load graph to future loads. Besides, a graph concatenation method for parallel training is introduced to improve the learning efficiency. Using practical data in Houston, the annual, monthly, and daily effects of the crisis on power load are analyzed, which uncovers the strong correlation between the pandemic and the changes in regional electricity utilization. Moreover, the forecasting performance of the load graph-based ResGCN is validated by comparing with other representative methods. Its performance on MAPE and RMSE increased by 1.3264 and 15.03%, respectively. Codes related to all the simulations are available on https://github.com/YoungY6/ResGCN-for-Short-term-power-load-forecasting-under-COVID-19.

Short Term Load Forecasting Based on PCA and LS-SVM

2013 ◽  
Vol 756-759 ◽  
pp. 4193-4197 ◽  
Author(s):  
Ren Ran Wei ◽  
Zhen Zhu Wei ◽  
Rong Rong ◽  
Yi Wang ◽  
Jian Dong Jiang ◽  
...  
Keyword(s):  
Load Forecasting ◽  
Influence Factors ◽  
Principal Component ◽  
Training Model ◽  
Influential Factors ◽  
Support Vector ◽  
Short Term ◽  
Power Load ◽  
Short Term Load Forecasting ◽  
Power Load Forecasting

In this paper, in order to improve the precision of the short-term load forecasting, we propose a power load forecasting method combined principal component analysis (PCA) with least squares support vector machine (LS-SVM). Firstly PCA extracts the feature of the influence factors for power load, and then LS-SVM constructs a training model with a new variables extracted by PCA. After using PCA-LS-SVM model this paper proposed to forecast power load of one area, the results show that this method can effectively eliminate the redundant information among influential factors, reduce the input dimension of the prediction model, simplify the structure of the network, increase the learning speed and improve the power load forecasting accuracy. So this method is effectively feasible.

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.

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