Study on Power Load Forecasting Method Based on Ant Colony Optimization

Load forecasting is one of the important working of the power system, which plays a very significant role in various departments of power system operation. Load accurate scientific prediction can make power decision-making departments economically and reasonably to adjust generator, power line, which makes it more reasonable. This paper introduces the optimal combination forecast model, and organically combine with several electric load forecasting models by the weight, come to more accurate results, with higher prediction accuracy, and the relative error is small, it has some practical value.

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Application of Combination Forecast Model in the Medium and Long Term Power Load Forecast

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.490-495.1362 ◽

2012 ◽

Vol 490-495 ◽

pp. 1362-1366 ◽

Cited By ~ 5

Author(s):

Ke Zhao ◽

Lin Gan ◽

Zhong Wang ◽

Yan Xiong

Keyword(s):

Load Forecasting ◽

Forecast Model ◽

Combination Forecast ◽

Combination Forecasting ◽

Power Load ◽

Forecasting Method ◽

Model Predictions ◽

High Prediction ◽

Predicted Values

For seasonal and long-term power load forecasting problem, this paper presents an optimal combination forecasting method, which can optimize the combination of multiple predictive models. Optimize the combination of the two model predictions with two models as an example, which are the gray GM(1,1) model and linear regression model, and finally compare the predicted values of combination with the real values. The results show that: the combination forecasting method has a high prediction accuracy, and the error is very small.

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Improving Load Forecasting of Electric Vehicle Charging Stations Through Missing Data Imputation

Energies ◽

10.3390/en13184893 ◽

2020 ◽

Vol 13 (18) ◽

pp. 4893

Author(s):

Byungsung Lee ◽

Haesung Lee ◽

Hyun Ahn

Keyword(s):

Power System ◽

Load Forecasting ◽

Imputation Method ◽

Forecasting Model ◽

System Operation ◽

Power System Operation ◽

Forecasting Accuracy ◽

Electric Vehicle Charging ◽

Ev Charging ◽

Imputation Approach

As the penetration of electric vehicles (EVs) accelerates according to eco-friendly policies, the impact of electric vehicle charging demand on a power distribution network is becoming significant for reliable power system operation. In this regard, accurate power demand or load forecasting is of great help not only for unit commitment problem considering demand response but also for long-term power system operation and planning. In this paper, we present a forecasting model of EV charging station load based on long short-term memory (LSTM). Besides, to improve the forecasting accuracy, we devise an imputation method for handling missing values in EV charging data. For the verification of the forecasting model and our imputation approach, performance comparison with several imputation techniques is conducted. The experimental results show that our imputation approach achieves significant improvements in forecasting accuracy on data with a high missing rate. In particular, compared to a strategy without applying imputation, the proposed imputation method results in reduced forecasting errors of up to 9.8%.

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The short-term load forecasting of electric power system based on combination forecast model

The 27th Chinese Control and Decision Conference (2015 CCDC) ◽

10.1109/ccdc.2015.7161993 ◽

2015 ◽

Cited By ~ 2

Author(s):

Peng Xiuyan ◽

Zhang Biao ◽

Cui Yanqing

Keyword(s):

Power System ◽

Electric Power ◽

Load Forecasting ◽

Forecast Model ◽

Electric Power System ◽

Short Term ◽

Combination Forecast ◽

Short Term Load Forecasting

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A Novel Short-Term Residential Electric Load Forecasting Method Based on Adaptive Load Aggregation and Deep Learning Algorithms

Energies ◽

10.3390/en14227820 ◽

2021 ◽

Vol 14 (22) ◽

pp. 7820

Author(s):

Tingting Hou ◽

Rengcun Fang ◽

Jinrui Tang ◽

Ganheng Ge ◽

Dongjun Yang ◽

...

Keyword(s):

Deep Learning ◽

Load Forecasting ◽

System Level ◽

Electric Load ◽

Short Term ◽

Power Load ◽

Forecasting Method ◽

Home Energy Management ◽

Aggregated Load ◽

Electric Load Forecasting

Short-term residential load forecasting is the precondition of the day-ahead and intra-day scheduling strategy of the household microgrid. Existing short-term electric load forecasting methods are mainly used to obtain regional power load for system-level power dispatch. Due to the high volatility, strong randomness, and weak regularity of the residential load of a single household, the mean absolute percentage error (MAPE) of the traditional methods forecasting results would be too big to be used for home energy management. With the increase in the total number of households, the aggregated load becomes more and more stable, and the cyclical pattern of the aggregated load becomes more and more distinct. In the meantime, the maximum daily load does not increase linearly with the increase in households in a small area. Therefore, in our proposed short-term residential load forecasting method, an optimal number of households would be selected adaptively, and the total aggregated residential load of the selected households is used for load prediction. In addition, ordering points to identify the clustering structure (OPTICS) algorithm are also selected to cluster households with similar power consumption patterns adaptively. It can be used to enhance the periodic regularity of the aggregated load in alternative. The aggregated residential load and encoded external factors are then used to predict the load in the next half an hour. The long short-term memory (LSTM) deep learning algorithm is used in the prediction because of its inherited ability to maintain historical data regularity in the forecasting process. The experimental data have verified the effectiveness and accuracy of our proposed method.

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