Day-ahead load forecasting using improved grey Verhulst model

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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Short-Term Load Forecasting for Electric Vehicle Charging Stations Based on Deep Learning Approaches

Applied Sciences ◽

10.3390/app9091723 ◽

2019 ◽

Vol 9 (9) ◽

pp. 1723 ◽

Cited By ~ 12

Author(s):

Juncheng Zhu ◽

Zhile Yang ◽

Yuanjun Guo ◽

Jiankang Zhang ◽

Huikun Yang

Keyword(s):

Deep Learning ◽

Power Systems ◽

Unit Commitment ◽

Combustion Engine ◽

Load Forecasting ◽

Learning Approaches ◽

Short Term ◽

Load Curve ◽

Short Term Load Forecasting ◽

Gated Recurrent Units

Short-term load forecasting is a key task to maintain the stable and effective operation of power systems, providing reasonable future load curve feeding to the unit commitment and economic load dispatch. In recent years, the boost of internal combustion engine (ICE) based vehicles leads to the fossil fuel shortage and environmental pollution, bringing significant contributions to the greenhouse gas emissions. One of the effective ways to solve problems is to use electric vehicles (EVs) to replace the ICE based vehicles. However, the mass rollout of EVs may cause severe problems to the power system due to the huge charging power and stochastic charging behaviors of the EVs drivers. The accurate model of EV charging load forecasting is, therefore, an emerging topic. In this paper, four featured deep learning approaches are employed and compared in forecasting the EVs charging load from the charging station perspective. Numerical results show that the gated recurrent units (GRU) model obtains the best performance on the hourly based historical data charging scenarios, and it, therefore, provides a useful tool of higher accuracy in terms of the hourly based short-term EVs load forecasting.

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

MATEC Web of Conferences ◽

10.1051/matecconf/20165506003 ◽

2016 ◽

Vol 55 ◽

pp. 06003 ◽

Cited By ~ 4

Author(s):

Kamal Chapagain ◽

Somsak Kittipiyakul

Keyword(s):

Bayesian Estimation ◽

Load Forecasting ◽

Forecasting Model ◽

Short Term ◽

Electricity Load ◽

Electricity Load Forecasting

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Short-Term Electricity Load Forecasting Model Based on EMD-GRU with Feature Selection

Energies ◽

10.3390/en12061140 ◽

2019 ◽

Vol 12 (6) ◽

pp. 1140 ◽

Cited By ~ 15

Author(s):

Xin Gao ◽

Xiaobing Li ◽

Bing Zhao ◽

Weijia Ji ◽

Xiao Jing ◽

...

Keyword(s):

Feature Selection ◽

Prediction Accuracy ◽

Load Forecasting ◽

Support Vector ◽

Forecasting Model ◽

Data Sets ◽

Short Term ◽

Model Based ◽

Electricity Load ◽

Electricity Load Forecasting

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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Online SARIMA applied for short-term electricity load forecasting

10.21203/rs.3.rs-439120/v1 ◽

2021 ◽

Author(s):

Quang Dat Nguyen ◽

Nhat Anh Nguyen ◽

Ngoc Thang Tran ◽

Vijender Kumar Solanki ◽

Rubén González Crespo ◽

...

Keyword(s):

Power Systems ◽

Moving Average ◽

Supply And Demand ◽

Load Forecasting ◽

Percentage Error ◽

Short Term ◽

Autoregressive Integrated Moving Average ◽

Proposed Model ◽

Short Term Load Forecasting ◽

Electricity Load

Abstract Short-term Load Forecasting (STLF) plays a crucial role in balancing supply and demand of load dispatching operation, ensures stability for the power system. With the advancement of real-time smart sensors in power systems, it is of great significance to develop techniques to handle data streams on-the-fly to improve operational efficiency. In this paper, we propose an online variant of Seasonal Autoregressive Integrated Moving Average (SARIMA) to forecast electricity load sequentially. The proposed model is utilized to forecast hourly electricity load of northern Vietnam and achieves a mean absolute percentage error (MAPE) of 4.57%.

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