Four methods for short-term load forecasting using the benefits of artificial intelligence

Erkmen, I.; Topalli, Ayca Kumluca

doi:10.1007/s00202-003-0163-9

Cited by 8 publications

(2 citation statements)

References 16 publications

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“…Erkmen, I. and Topalli, A.K. 2003 [8] carried out short-term load forecasting based on four methods depending on AI (artificial intelligence), backpropagation learning algorithm momentum-based weight updating multilayer perceptron network lead better results with a low error value. López et al 2018 [9] pointed out autoregressive and neural network-based short-term load forecasting and performed a performance comparison.…”

Section: Literature Studiesmentioning

confidence: 99%

Different Forecasting Horizons Based Performance Analysis of Electricity Load Forecasting Using Multilayer Perceptron Neural Network

Madhiarasan

Louzazni

2021

Forecasting

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With an uninterrupted power supply to the consumer, it is obligatory to balance the electricity generated by the electricity load. The effective planning of economic dispatch, reserve requirements, and quality power provision for accurate consumer information concerning the electricity load is needed. The burden on the power system engineers eased electricity load forecasting is essential to ensure the enhanced power system operation and planning for reliable power provision. Fickle nature, atmospheric parameters influence makes electricity load forecasting a very complex and challenging task. This paper proposed a multilayer perceptron neural network (MLPNN) with an association of recursive fine-tuning strategy-based different forecasting horizons model for electricity load forecasting. We consider the atmospheric parameters as the inputs to the proposed model, overcoming the atmospheric effect on electricity load forecasting. Hidden layers and hidden neurons based on performance investigation performed. Analyzed performance of the proposed model with other existing models; the comparative performance investigation reveals that the proposed forecasting model performs rigorous with a minimal evaluation index (mean square error (MSE) of 1.1506 × 10−05 for Dataset 1 and MSE of 4.0142 × 10−07 for Dataset 2 concern to the single hidden layer and MSE of 2.9962 × 10−07 for Dataset 1, and MSE of 1.0425 × 10−08 for Dataset 2 concern to two hidden layers based proposed model) and compared to the considered existing models. The proposed neural network possesses a good forecasting ability because we develop based on various atmospheric parameters as the input variables, which overcomes the variance. It has a generic performance capability for electricity load forecasting. The proposed model is robust and more reliable.

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Section: Literature Studiesmentioning

confidence: 99%

Different Forecasting Horizons Based Performance Analysis of Electricity Load Forecasting Using Multilayer Perceptron Neural Network

Madhiarasan

Louzazni

2021

Forecasting

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“…It has become a mature way to use historical load data and influencing factors as the data basis of a load forecasting model. Supplemented by a variety of artificial intelligence algorithms with excellent data fitting ability [8][9][10], such as vector autoregression, support vector machine [11], neural network [12], and deep learning [13], can usually obtain satisfactory forecasting accuracy. These algorithms have their advantages in terms of feature extraction and learning ability and have useful application value in load forecasting.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Electric Load Forecasting of Integrated Energy System Considering Nonlinear Synergy Between Different Loads

Wang

2021

IEEE Access

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As an efficient form of energy utilization, an integrated energy system integrates oil, natural gas, coal, and other energy sources and converts them into electric, cooling, and heating for users through energy conversion devices. In this process, integrated energy service providers need to make energy conversion decisions based on users' demand information feedback. Therefore, there is uncertainty and coupling between the electric cooling and heating loads, making it difficult to forecast the loads accurately. Firstly, this paper analyzes the integrated energy system's energy consumption characteristics and the interaction mechanism between the supply and demand sides, which fundamentally explains the coupling relationship between different loads of the integrated energy system. Secondly, REC, DEC, REH, and DEH are constructed from electric cooling and heating loads. The relationship between electric load and cooling and heating load is analyzed by a scatter distribution diagram and maximum information coefficient method. The nonlinear correlation between electric load and cooling and heating loads is proved. Based on this, the integrated energy system's synergetic electric load forecasting formula reflecting the nonlinear synergistic effect between loads is proposed. Finally, based on stacking ensemble learning, an integrated energy system electric load forecasting model considering the nonlinear synergy between loads is established by integrating BP neural network, support vector regression, random forest, and gradient boosting decision tree. Through the experimental analysis of the Arizona State University Tempe campus's integrated energy system project, it is found that the effect of the synergistic quadratic forecasting is better than that of the primary forecasting. Besides, the MAPE of the quadratic synergistic forecasting formula is lower than that of the other two forms, indicating that the proposed synergistic electric load forecasting formula considering the nonlinear synergy between loads can improve the accuracy of electric load forecasting of the integrated energy system.INDEX TERMS energy consumption characteristics, integrated energy system, synergetic forecasting, Stacking ensemble learning.

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Bat algorithm-based back propagation approach for short-term load forecasting considering weather factors

Reddy

2017

Electr Eng

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Four methods for short-term load forecasting using the benefits of artificial intelligence

Cited by 8 publications

References 16 publications

Different Forecasting Horizons Based Performance Analysis of Electricity Load Forecasting Using Multilayer Perceptron Neural Network

Different Forecasting Horizons Based Performance Analysis of Electricity Load Forecasting Using Multilayer Perceptron Neural Network

Short-Term Electric Load Forecasting of Integrated Energy System Considering Nonlinear Synergy Between Different Loads

Bat algorithm-based back propagation approach for short-term load forecasting considering weather factors

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