Online short-term load forecasting methods using hybrids of single multiplicative neuron model, particle swarm optimization variants and nonlinear filters

Wu, Xuedong; Wang, Yaonan; Bai, Yingjie; Zhu, Zhiyu; Xia, Aiming

doi:10.1016/j.egyr.2021.01.030

Cited by 19 publications

(10 citation statements)

References 44 publications

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“…2 In the literature, parameters that affect electricity consumption include the economy, industry, climate, ambient temperature, solar radiation, humidity, wind speed, working days, and the gross domestic product. 29,30 However, apart from these classical studies, no examination of extraordinary situations such as pandemics has been carried out. Moreover, for countries that have followed a partial closure system, regional or city-based studies are important in obtaining critical results.…”

Section: Contribution Of the Articlementioning

confidence: 99%

“…In this context, energy supply security could be ensured through government policies and maintenance‐investment plans 2 . In the literature, parameters that affect electricity consumption include the economy, industry, climate, ambient temperature, solar radiation, humidity, wind speed, working days, and the gross domestic product 29,30 . However, apart from these classical studies, no examination of extraordinary situations such as pandemics has been carried out.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of electricity energy consumption including COVID‐19 precautions using the hybrid MLR‐FFANN optimized with the stochastic fractal search with fitness distance balance algorithm

Dalcalı

Özbay

Duman

2022

Concurrency and Computation

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The increase in energy consumption is affected by the developments in technology as well as the global population growth. Increasing energy consumption makes it difficult to ensure electrical energy supply security. Meeting the energy demand can be achieved with the right planning. Proper planning is critical for both economical use of resources and low cost for the end consumer. On the other hand, erroneous estimation of demand may cause waste of resources and energy crisis. Accurate estimation is possible by accurately modeling the factors affecting electricity consumption. Apart from known factors such as seasonal conditions, days of the week and hours, modeling in extreme events such as pandemics that affect all our behaviors increases the success in modeling the future projection. This ensures that the security of electrical energy supply is carried out effectively with limited resources. For this purpose, in this study, a hybrid multiple linear regression-feedforward artificial neural network (MLR-FFANN) based algorithm model was proposed, taking into account the estimated impact of the COVID-19 pandemic on the energy consumption values of Bursa, an industrial city in Turkey. The aim of the hybrid MLR-FFANN approach was to simultaneously optimize the β polynomial for multiple linear regression and the weight and bias coefficients for the forward propagation neural network using the adaptive guided differential evolution, equilibrium optimizer, slime mold algorithm, and stochastic fractal search with fitness distance balance (SFSFDB) optimization algorithms. The success of the model whose parameters were optimized using the optimization algorithms was determined according to mean absolute error, mean absolute percentage error, and root mean square error evaluation criteria and statistical analysis of these results. According to the results of the analysis, the MLR-FFANN approach whose parameters were optimized with the SFSFDB algorithm was more successful in the training of the dataset containing the COVID-19 precautions.

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Section: Contribution Of the Articlementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of electricity energy consumption including COVID‐19 precautions using the hybrid MLR‐FFANN optimized with the stochastic fractal search with fitness distance balance algorithm

Dalcalı

Özbay

Duman

2022

Concurrency and Computation

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show abstract

“…In the literature studies, the factors affecting electricity consumption include parameters such as the economy, industry, climate, ambient temperature, solar radiation, humidity, wind speed, working day, and gross domestic product. 3,4 Estimation methods used in the literature are based on user experience or mathematical modeling. Mathematical modeling includes methods such as regression analysis, multiple regression, curve fitting, least squares technique, and the Box-Jenkins approach.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Overestimating energy demand can lead to unnecessary investment, whereas underestimating it can lead to energy crises. In the literature studies, the factors affecting electricity consumption include parameters such as the economy, industry, climate, ambient temperature, solar radiation, humidity, wind speed, working day, and gross domestic product 3,4 . Estimation methods used in the literature are based on user experience or mathematical modeling.…”

Section: Introductionmentioning

confidence: 99%

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Manta ray foraging optimization algorithm–based feedforward neural network for electric energy consumption forecasting

Duman

Dalcalı

Özbay

2021

Int Trans Electr Energ Syst

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Multiplicative neuron models for very short-term load forecasting

Joshi,

Yadav

2024

Electr Eng

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Load forecasting has always been a crucial component of operational and managerial aspect of e cient power system planning. Since there are several factors on which load forecasting depends, it becomes necessary to nd out the level of impact these factors put on it. In the study, data preparation is performed by transforming the historical electric load of Uttarakhand state adopting Max-Min normalization. The data generated was partitioned into categories of training and testing data for further application of the conventional and different multiplicative neuron models. As per the Akaike information criterion QIFNM is the best t model among all. The QIFNM having single neuron and different parameters achieved MSE of 0.0020. The performance analysis of studied model revealed that a single neuron of QIFNM can be used for VSTLF in relation to a particular geographic area with same set of condition with better e ciency as of several neurons of the conventional model.

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Online short-term load forecasting methods using hybrids of single multiplicative neuron model, particle swarm optimization variants and nonlinear filters

Cited by 19 publications

References 44 publications

Prediction of electricity energy consumption including COVID‐19 precautions using the hybrid MLR‐FFANN optimized with the stochastic fractal search with fitness distance balance algorithm

Prediction of electricity energy consumption including COVID‐19 precautions using the hybrid MLR‐FFANN optimized with the stochastic fractal search with fitness distance balance algorithm

Manta ray foraging optimization algorithm–based feedforward neural network for electric energy consumption forecasting

Multiplicative neuron models for very short-term load forecasting

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