Short-term Electricity Load Forecasting Model and Bayesian Estimation for Thailand Data

Many factors affect short-term electric load, and the superposition of these factors leads to it being non-linear and non-stationary. Separating different load components from the original load series can help to improve the accuracy of prediction, but the direct modeling and predicting of the decomposed time series components will give rise to multiple random errors and increase the workload of prediction. This paper proposes a short-term electricity load forecasting model based on an empirical mode decomposition-gated recurrent unit (EMD-GRU) with feature selection (FS-EMD-GRU). First, the original load series is decomposed into several sub-series by EMD. Then, we analyze the correlation between the sub-series and the original load series through the Pearson correlation coefficient method. Some sub-series with high correlation with the original load series are selected as features and input into the GRU network together with the original load series to establish the prediction model. Three public data sets provided by the U.S. public utility and the load data from a region in northwestern China were used to evaluate the effectiveness of the proposed method. The experiment results showed that the average prediction accuracy of the proposed method on four data sets was 96.9%, 95.31%, 95.72%, and 97.17% respectively. Compared to a single GRU, support vector regression (SVR), random forest (RF) models and EMD-GRU, EMD-SVR, EMD-RF models, the prediction accuracy of the proposed method in this paper was higher.

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Analysis of temperature-sensitive on short-term electricity load forecasting

2020 IEEE-HYDCON ◽

10.1109/hydcon48903.2020.9242910 ◽

2020 ◽

Author(s):

Nguyen Xuan Tung ◽

Nguyen Quang Dat ◽

Tran Ngoc Thang ◽

Vijender Kumar Solanki ◽

Nguyen Thi Ngoc Anh

Keyword(s):

Load Forecasting ◽

Temperature Sensitive ◽

Short Term ◽

Electricity Load ◽

Electricity Load Forecasting

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Deep-learning-based short-term electricity load forecasting: A real case application

Engineering Applications of Artificial Intelligence ◽

10.1016/j.engappai.2021.104645 ◽

2022 ◽

Vol 109 ◽

pp. 104645

Author(s):

Ibrahim Yazici ◽

Omer Faruk Beyca ◽

Dursun Delen

Keyword(s):

Deep Learning ◽

Load Forecasting ◽

Real Case ◽

Short Term ◽

Electricity Load ◽

Case Application ◽

Electricity Load Forecasting

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Comparative Analysis of Statistical, Machine Learning, and Grey Methods for Short-Term Electricity Load Forecasting

Nature-Inspired Computing ◽

10.4018/978-1-5225-0788-8.ch044 ◽

2016 ◽

pp. 1161-1183 ◽

Cited By ~ 1

Author(s):

Tuncay Ozcan ◽

Tarik Küçükdeniz ◽

Funda Hatice Sezgin

Keyword(s):

Support Vector Regression ◽

Electricity Market ◽

Load Forecasting ◽

Forecast Accuracy ◽

Grey Model ◽

Support Vector ◽

Forecasting Model ◽

Data Sets ◽

Electricity Load ◽

Electricity Load Forecasting

Electricity load forecasting is crucial for electricity generation companies, distributors and other electricity market participants. In this study, several forecasting techniques are applied to time series modeling and forecasting of the hourly loads. Seasonal grey model, support vector regression, random forests, seasonal ARIMA and linear regression are benchmarked on seven data sets. A rolling forecasting model is developed and 24 hours of the next day is predicted for the last 14 days of each data set. This day-ahead forecasting model is especially important in day-ahead market activities and plant scheduling operations. Experimental results indicate that support vector regression and seasonal grey model outperforms other approaches in terms of forecast accuracy for day-ahead load forecasting.

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Day-ahead load forecasting using improved grey Verhulst model

Journal of Engineering Design and Technology ◽

10.1108/jedt-12-2019-0337 ◽

2020 ◽

Vol 18 (5) ◽

pp. 1335-1348

Author(s):

Ariel Mutegi Mbae ◽

Nnamdi I. Nwulu

Keyword(s):

Power Systems ◽

Unit Commitment ◽

Load Forecasting ◽

Forecasting Model ◽

Short Term ◽

Content Type ◽

Dispatch System ◽

System Maintenance ◽

Electricity Load ◽

Generator Unit

Purpose In the daily energy dispatch process in a power system, accurate short-term electricity load forecasting is a very important tool used by spot market players. It is a critical requirement for optimal generator unit commitment, economic dispatch, system security and stability assessment, contingency and ancillary services management, reserve setting, demand side management, system maintenance and financial planning in power systems. The purpose of this study is to present an improved grey Verhulst electricity load forecasting model. Design/methodology/approach To test the effectiveness of the proposed model for short-term load forecast, studies made use of Kenya’s load demand data for the period from January 2014 to June 2019. Findings The convectional grey Verhulst forecasting model yielded a mean absolute percentage error of 7.82 per cent, whereas the improved model yielded much better results with an error of 2.96 per cent. Practical implications In the daily energy dispatch process in a power system, accurate short-term load forecasting is a very important tool used by spot market players. It is a critical ingredient for optimal generator unit commitment, economic dispatch, system security and stability assessment, contingency and ancillary services management, reserve setting, demand side management, system maintenance and financial planning in power systems. The fact that the model uses actual Kenya’s utility data confirms its usefulness in the practical world for both economic planning and policy matters. Social implications In terms of generation and transmission investments, proper load forecasting will enable utilities to make economically viable decisions. It forms a critical cog of the strategic plans for power utilities and other market players to avoid a situation of heavy stranded investment that adversely impact the final electricity prices and the other extreme scenario of expensive power shortages. Originality/value This research combined the use of natural logarithm and the exponential weighted moving average to improve the forecast accuracy of the grey Verhulst forecasting model.

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