Bus ultra-short-term load forecasting considering the impact of distributed photovoltaic power supply

As objects of load prediction are becoming increasingly diversified and complicated, it is extremely important to improve the accuracy of load forecasting under complex systems. When using the group method of data handling (GMDH), it is easy for the load forecasting to suffer from overfitting and be unable to deal with multicollinearity under complex systems. To solve this problem, this paper proposes a GMDH algorithm based on elastic net regression, that is, group method of data handling based on elastic net (EN-GMDH), as a short-term load forecasting model. The algorithm uses an elastic net to compress and punish the coefficients of the Kolmogorov–Gabor (K–G) polynomial and select variables. Meanwhile, based on the difference degree of historical data, this paper carries out variable weight processing on the input data of load forecasting, so as to solve the impact brought by the abrupt change of load law. Ten characteristic variables, including meteorological factors, meteorological accumulation factors, and holiday factors, are taken as input variables. Then, EN-GMDH is used to establish the relationship between the characteristic variables and the load, and a short-term load forecasting model is established. The results demonstrate that, compared with other algorithms, the evaluation index of EN-GMDH is significantly better than that of the rest algorithm models in short-term load forecasting, and the accuracy of prediction is obviously improved.

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Research for Short-Term Load Forecasting Based on Linearization Meteorological Factors

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.596.700 ◽

2014 ◽

Vol 596 ◽

pp. 700-703

Author(s):

Shun Hua Zhang

Keyword(s):

Factor Model ◽

Meteorological Factors ◽

Load Forecasting ◽

Meteorological Factor ◽

Short Term ◽

Power Load ◽

Short Term Load Forecasting ◽

Electricity Load ◽

Heating Load ◽

The Impact

With the development of economy in recent years, rapid growth of electricity demand, the cooling and heating load gets more and more big proportion of the total electricity load; the power load is influenced by meteorological factors which become more and more big. This topic will be based on short-term load forecasting in ANN (Artificial Neural Networks), conduct further research on the relationship between meteorological factors and power load, find the impact of the core meteorological factors of power load, and linear core meteorological factor model to establish the suitable for load forecasting based on ANN, make the forecasting to correctly reflect the meteorological conditions, improve the prediction accuracy of short-term load forecasting.

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Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study

Energies ◽

10.3390/en14134046 ◽

2021 ◽

Vol 14 (13) ◽

pp. 4046

Author(s):

Andrei M. Tudose ◽

Irina I. Picioroaga ◽

Dorian O. Sidea ◽

Constantin Bulac ◽

Valentin A. Boicea

Keyword(s):

Power Systems ◽

Mean Squared Error ◽

Load Forecasting ◽

Absolute Error ◽

Percentage Error ◽

Linear Regression Method ◽

Short Term ◽

Short Term Load Forecasting ◽

The Mean ◽

The Impact

Short-term load forecasting (STLF) is fundamental for the proper operation of power systems, as it finds its use in various basic processes. Therefore, advanced calculation techniques are needed to obtain accurate results of the consumption prediction, taking into account the numerous exogenous factors that influence the results’ precision. The purpose of this study is to integrate, additionally to the conventional factors (weather, holidays, etc.), the current aspects regarding the global COVID-19 pandemic in solving the STLF problem, using a convolutional neural network (CNN)-based model. To evaluate and validate the impact of the new variables considered in the model, the simulations are conducted using publicly available data from the Romanian power system. A comparison study is further carried out to assess the performance of the proposed model, using the multiple linear regression method and load forecasting results provided by the Romanian Transmission System Operator (TSO). In this regard, the Mean Squared Error (MSE), the Mean Absolute Error (MAE), the Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) are used as evaluation indexes. The proposed methodology shows great potential, as the results reveal better error values compared to the TSO results, despite the limited historical data.

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Forecasting the Short-Term Electric Load Considering the Influence of Air Pollution Prevention and Control Policy via a Hybrid Model

Sustainability ◽

10.3390/su11102983 ◽

2019 ◽

Vol 11 (10) ◽

pp. 2983 ◽

Cited By ~ 2

Author(s):

Xueliang Li ◽

Bingkang Li ◽

Long Zhao ◽

Huiru Zhao ◽

Wanlei Xue ◽

...

Keyword(s):

Air Pollution ◽

Prevention And Control ◽

Load Forecasting ◽

Pollution Prevention ◽

Support Vector ◽

Short Term ◽

Short Term Load Forecasting ◽

Pollution Prevention And Control ◽

And Control ◽

The Impact

Since 2013, a series of air pollution prevention and control (APPC) measures have been promulgated in China for reducing the level of air pollution, which can affect regional short-term electricity power demand by changing the behavior of power users electricity consumption. This paper analyzes the policy system of the APPC measures and its impact on regional short-term electricity demand, and determines the regional short-term load impact factors considering the impact of APPC measures. On this basis, this paper proposes a similar day selection method based on the best and worst method and grey relational analysis (BWM-GRA) in order to construct the training sample set, which considers the difference in the influence degree of characteristic indicators on daily power load. Further, a short-term load forecasting method based on least squares support vector machine (LSSVM) optimized by salp swarm algorithm (SSA) is developed. By forecasting the load of a city affected by air pollution in Northern China, and comparing the results with several selected models, it reveals that the impact of APPC measures on regional short-term load is significant. Moreover, by considering the influence of APPC measures and avoiding the subjectivity of model parameter settings, the proposed load forecasting model can improve the accuracy of, and provide an effective tool for short-term load forecasting. Finally, some limitations of this paper are discussed.

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The method of short-term load forecasting based on the RBF neural network

18th International Conference and Exhibition on Electricity Distribution (CIRED 2005) ◽

10.1049/cp:20051341 ◽

2005 ◽

Author(s):

Yan Lu ◽

Xin Lin ◽

Weifu Qi

Keyword(s):

Neural Network ◽

Rbf Neural Network ◽

Load Forecasting ◽

Short Term ◽

Short Term Load Forecasting

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Research on neural network model based on maximum conditional entropy and its application on short term load forecasting

2006 China International Conference on Electricity Distribution (CICED 2006) ◽

10.1049/cp:20061766 ◽

2006 ◽

Author(s):

Jianchang Lu ◽

Wei Sun ◽

Ming Meng

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Load Forecasting ◽

Conditional Entropy ◽

Short Term ◽

Model Based ◽

Short Term Load Forecasting

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Short-term load forecasting using Theta method

E3S Web of Conferences ◽

10.1051/e3sconf/20198401004 ◽

2019 ◽

Vol 84 ◽

pp. 01004 ◽

Cited By ~ 1

Author(s):

Grzegorz Dudek

Keyword(s):

Empirical Studies ◽

Load Forecasting ◽

Seasonal Cycles ◽

Short Term ◽

Forecasting Accuracy ◽

Forecasting Methods ◽

Short Term Load Forecasting ◽

Data Definition ◽

System Data ◽

Theta Method

The Theta method attracted the attention of researchers and practitioners in recent years due to its simplicity and superior forecasting accuracy. Its performance has been confirmed by many empirical studies as well as forecasting competitions. In this article the Theta method is tested in short-term load forecasting problem. The load time series expressing multiple seasonal cycles is decomposed in different ways to simplify the forecasting problem. Four variants of input data definition are considered. The standard Theta method is uses as well as the dynamic optimised Theta model proposed recently. The performances of the Theta models are demonstrated through an empirical application using real power system data and compared with other popular forecasting methods.

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