Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
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Spatio-temporal Load Forecasting Considering Aggregation Features of Electricity Cells and Uncertainties in Input Variables

  • Zhao, Teng (Dept. of Electrical Engineering, Shanghai Jiao Tong University) ;
  • Zhang, Yan (Dept. of Electrical Engineering, Shanghai Jiao Tong University) ;
  • Chen, Haibo (State Grid Shanghai Municipal Power Company)
  • Received : 2016.10.01
  • Accepted : 2017.08.09
  • Published : 2018.01.01
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Abstract

Spatio-temporal load forecasting (STLF) is a foundation for building the prediction-based power map, which could be a useful tool for the visualization and tendency assessment of urban energy application. Constructing one point-forecasting model for each electricity cell in the geographic space is possible; however, it is unadvisable and insufficient, considering the aggregation features of electricity cells and uncertainties in input variables. This paper presents a new STLF method, with a data-driven framework consisting of 3 subroutines: multi-level clustering of cells considering their aggregation features, load regression for each category of cells based on SLS-SVRNs (sparse least squares support vector regression networks), and interval forecasting of spatio-temporal load with sampled blind number. Take some area in Pudong, Shanghai as the region of study. Results of multi-level clustering show that electricity cells in the same category are clustered in geographic space to some extent, which reveals the spatial aggregation feature of cells. For cellular load regression, a comparison has been made with 3 other forecasting methods, indicating the higher accuracy of the proposed method in point-forecasting of spatio-temporal load. Furthermore, results of interval load forecasting demonstrate that the proposed prediction-interval construction method can effectively convey the uncertainties in input variables.

Keywords

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Fig. 1. Procedure of proposed STLF method

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Fig. 2. Flowchart of cellular load regression

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Fig. 3. Structural diagram of SLS-SVRNs algorithm

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Fig. 4. Schematic diagram for confidence interval evaluation

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Fig. 5. Map of the service area after cellular division

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Fig. 6. Evolution of cellular peak load in terms of land-usetype and distance to main roads

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Fig. 7. Results of multi-level cell clustering

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Fig. 8. Number of samples for each forecasting model

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Fig. 9. Spatio-temporal load forecasting results andforecasting error of the service area in 2013, 2014and 2015

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Fig. 10. MAPE (cell-fixed) of STLF results using differentmethods

Table 1. Simulation data sets of the test case.

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Table 2. MAPE (year-fixed) of STLF results using different methods

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Table 3. Interval forecasts of input variables (external properties)

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Table 4. Sampled blind number of input variables (external properties)

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Table 5. Evaluation of interval load forecasting.

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