Power demand forecasting for demand-driven energy production with biogas plants

Dittmer, Celina; Krümpel, Johannes; Lemmer, Andreas

doi:10.1016/j.renene.2020.10.099

Cited by 26 publications

(10 citation statements)

References 11 publications

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“…Consumption estimation can be performed with mathematical modeling such as least squares, regression analysis, artificial intelligence, or machine learning approaches including the genetic algorithm and fuzzy logic. 3,4 In the literature, many estimation methods have been presented comparatively for different time periods. Regression methods, Kalman filter-based forecasting, artificial intelligence techniques, deep recurrent neural networks, and hybrids of these techniques have been used successfully in the forecasting of electricity demand.…”

Section: Related Workmentioning

confidence: 99%

“…Prediction of electricity consumption over the short, medium, and long term can be carried out in any city, country, building, or structure. Consumption estimation can be performed with mathematical modeling such as least squares, regression analysis, artificial intelligence, or machine learning approaches including the genetic algorithm and fuzzy logic 3,4 . In the literature, many estimation methods have been presented comparatively for different time periods.…”

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: Related Workmentioning

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

“…5 Flexible computing methods based on user experience such as the genetic algorithm (GA), fuzzy logic, and neural networks are also used. [6][7][8] To predict short-term electricity demand, Vu et al proposed an autoregressive-based time varying model and determined that this model performed better than traditional seasonal autoregressive and neural network models in short-term electricity forecasting. 9 Muhanad et al performed electricity demand forecasting using Autoregressive Integrated Moving Average, Multivariate Adaptive Regression Spline (MARS), and Support Vector Regression (SVR) methods.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this step, using spiraling movements, each manta ray follows the one in front of it, while at the same time following the food. The model for this situation is given in Equation (8).…”

Section: Cyclone Foragingmentioning

confidence: 99%

“…Mathematical modeling includes methods such as regression analysis, multiple regression, curve fitting, least squares technique, and the Box–Jenkins approach 5 . Flexible computing methods based on user experience such as the genetic algorithm (GA), fuzzy logic, and neural networks are also used 6‐8 . To predict short‐term electricity demand, Vu et al proposed an autoregressive‐based time varying model and determined that this model performed better than traditional seasonal autoregressive and neural network models in short‐term electricity forecasting 9 .…”

Section: Introductionmentioning

confidence: 99%

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