Applying statistical analysis for assessing the reliability of input data to improve the quality of short-term load forecasting for a Ho Chi Minh City distribution network

Short-term load forecasting has an extremely important role in the design, operation and planning of power system, especially on a power grid of Ho Chi Minh City (HCMC) - an active city has the highest power demand in Vietnam. Through the data survey, the load power in the HCMC area changes suddenly so that it causes disturbances in the load data. Accordingly, the reliability assessment of the load data will be essential in the processing stage of data-filtering before implementing load forecasting models. This study introduces a novel statistical data-filtering method that takes into account the reliability of the input-data source by analyzing many different confidence levels. Results of the proposed data-filtering method will be compared to previous data -iltering methods (such as Kalman, DBSCAN, Wavelet Transform and SSA filtering methods). The data source used in this study was collected from more than 50 substations uisng the SCADA system in Ho Chi Minh City's distribution network and was put into a neural network prediction model - ANN (Artificial Neural Network) and a ARIMA model (Autoregressive Integrated Moving Average), to demonstrate the effectiveness of the proposed data-filtering method. Numerical results derived from ANN and ARIMA predictive models show the effectiveness of the proposed data-filtering method, particularly, when the reliability of real data from the Ho Chi Minh city distribution network is determined at the 95% level, the forecasting results of ANN and ARIMA models using the proposed data-filtering method are obviously better than that without filtering method or using other data-filtering methods.

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A comparative study of short-term load forecasting methods in distribution network

Journal of Science, Quy Nhon University ◽

10.52111/qnjs.2021.15103 ◽

2021 ◽

Vol 15 (1) ◽

pp. 23-35

Author(s):

Tuan Ho Le ◽

◽

Quang Hung Le ◽

Thanh Hoang Phan

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Artificial Neural Network ◽

Arima Model ◽

Distribution Network ◽

Load Forecasting ◽

Short Term ◽

Load Demand ◽

Forecasting Methods ◽

Short Term Load Forecasting

Short-term load forecasting plays an important role in building operation strategies and ensuring reliability of any electric power system. Generally, short-term load forecasting methods can be classified into three main categories: statistical approaches, artificial intelligence based-approaches and hybrid approaches. Each method has its own advantages and shortcomings. Therefore, the primary objective of this paper is to investigate the effectiveness of ARIMA model (e.g., statistical method) and artificial neural network (e.g., artificial intelligence based-method) in short-term load forecasting of distribution network. Firstly, the short-term load demand of Quy Nhon distribution network and short-term load demand of Phu Cat distribution network are analyzed. Secondly, the ARIMA model is applied to predict the load demand of two distribution networks. Thirdly, the artificial neural network is utilized to estimate the load demand of these networks. Finally, the estimated results from two applied methods are conducted for comparative purposes.

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Accuracy improvement of various short-term load forecasting models by a novel and unified statistical data-filtering method

International Journal of Green Energy ◽

10.1080/15435075.2020.1761810 ◽

2020 ◽

Vol 17 (7) ◽

pp. 382-406

Author(s):

Duong Minh Bui ◽

Phuc Duy Le ◽

Minh Tien Cao ◽

Trang Thi Pham ◽

Duy Anh Pham

Keyword(s):

Statistical Data ◽

Load Forecasting ◽

Data Filtering ◽

Short Term ◽

Accuracy Improvement ◽

Filtering Method ◽

Forecasting Models ◽

Short Term Load Forecasting

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Using a Self Organizing Map Neural Network for Short-Term Load Forecasting, Analysis of Different Input Data Patterns

Advances in Intelligent and Soft Computing - Distributed Computing and Artificial Intelligence ◽

10.1007/978-3-642-14883-5_51 ◽

2010 ◽

pp. 397-400 ◽

Cited By ~ 4

Author(s):

C. Senabre ◽

S. Valero ◽

J. Aparicio

Keyword(s):

Neural Network ◽

Input Data ◽

Load Forecasting ◽

Self Organizing Map ◽

Short Term ◽

Short Term Load Forecasting ◽

Forecasting Analysis ◽

Self Organizing

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Short-Term Load Forecasting Enhanced With Statistical Data-Filtering Method

2020 IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy (PESGRE2020) ◽

10.1109/pesgre45664.2020.9070497 ◽

2020 ◽

Author(s):

Minh Tien Cao ◽

Trang Thi Pham ◽

Tsai Chi Kuo ◽

Duong Minh Bui ◽

Huu Vinh Nguyen ◽

...

Keyword(s):

Statistical Data ◽

Load Forecasting ◽

Data Filtering ◽

Short Term ◽

Filtering Method ◽

Short Term Load Forecasting

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Research on Ultra-Short-Term Load Forecasting of Distribution Network Based on Fuzzy Clustering and RBF Neural Network

E3S Web of Conferences ◽

10.1051/e3sconf/202021303002 ◽

2020 ◽

Vol 213 ◽

pp. 03002

Author(s):

Guozhen Ma ◽

Po Hu ◽

Yunjia Wang ◽

Yongli Wang ◽

Chengcong Cai ◽

...

Keyword(s):

Neural Network ◽

Fuzzy Clustering ◽

Rbf Neural Network ◽

Distribution Network ◽

Load Forecasting ◽

Current Load ◽

Short Term ◽

Forecast Period ◽

Short Term Load Forecasting ◽

Load Characteristics

In order to solve the diversification of the load characteristics of the distribution network due to the difference in the electric structure and the electricity consumption habits of users, the calculation accuracy of the forecast model is difficult to meet the actual demand. In this paper, through in-depth study of the characteristics of ultra-short-term load, an ultra-short-term load forecasting model based on fuzzy clustering and RBF neural network (FCM-RBF) is constructed. The model not only considers the historical load characteristics of locally similar days, but also considers the current load characteristics of the forecast days. The load on a locally similar day can well reflect the overall trend of the predicted load; the current load on the forecast day can well reflect the changing law of real-time data during the forecast period and some random factors in the forecast period. Finally, a power grid load in a certain area of southwestern China is selected as an example to verify the effectiveness and accuracy of the proposed method.

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