Long-term load forecasting: models based on MARS, ANN and LR methods

Nalcaci, Gamze; Özmen, Ayşe; Weber, Gerhard

doi:10.1007/s10100-018-0531-1

Cited by 106 publications

(49 citation statements)

References 31 publications

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“…Electric is a clean and efficient source of energy, which plays an irreplaceable role in our daily life [1]. The significance of electricity has been increasing drastically recently [2], and therefore it has become an essential subject in research [3]. Besides, electric power is more suitable and efficient for the requirement of environment-friendly society compared with other traditional sources of energy such as natural gas, coal, and oil [1].…”

Section: Introductionmentioning

confidence: 99%

“…However, electricity as a product has different characteristics compared to material products since it cannot be stored in bulk as it should be generated as soon as it is demanded [2,4]. Also, the demand pattern of electricity is complex due to the deregulation of electricity markets such as electricity oversupply and shortage, which could lead to inaccurate forecasting and causes significant financial loss [3,4]. Moreover, the global electricity demand is expected to multiply with increasing population and living standards improvement.…”

Section: Introductionmentioning

confidence: 99%

“…Also, economies expand as well as using high-power electrical appliances and developing technology such as smart grids, electric cars, and renewable energy production. All these factors make it difficult to manage the power system [3,5,6]. Hence, it is necessary to predict the needs/loads of electricity in advance and before deciding on the generation of it [7].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Methods and Models for Electric Load Forecasting: A Comprehensive Review

Hammad

Jereb

Rosi

et al. 2020

Logistics &Amp; Sustainable Transport

156

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Electric load forecasting (ELF) is a vital process in the planning of the electricity industry and plays a crucial role in electric capacity scheduling and power systems management and, therefore, it has attracted increasing academic interest. Hence, the accuracy of electric load forecasting has great importance for energy generating capacity scheduling and power system management. This paper presents a review of forecasting methods and models for electricity load. About 45 academic papers have been used for the comparison based on specified criteria such as time frame, inputs, outputs, the scale of the project, and value. The review reveals that despite the relative simplicity of all reviewed models, the regression analysis is still widely used and efficient for long-term forecasting. As for short-term predictions, machine learning or artificial intelligence-based models such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Fuzzy logic are favored.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Methods and Models for Electric Load Forecasting: A Comprehensive Review

Hammad

Jereb

Rosi

et al. 2020

Logistics &Amp; Sustainable Transport

156

View full text Add to dashboard Cite

show abstract

“…Its convergence efficiency may also affect the accuracy of wind speed forecast. On the other hand, a SVM model was reported to improve the function of fluctuating wind speed forecast [14]. Its generalization ability, however, was not enough for the wind speed prediction performance.…”

Section: Introductionmentioning

confidence: 99%

“…If yes, the IMF, denoted as Repeat the same process from the first step until ) (t r k , as shown in equation (13), becomes a monotone function. (7) Finally, ( ) u t is decomposed into k IMFs with different oscillation modes, as shown inEquation(14).…”

mentioning

confidence: 99%

Optimization of Feedforward Neural Networks Using an Improved Flower Pollination Algorithm for Short-Term Wind Speed Prediction

Ren

Lin

2019

Energies

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It is well known that the inherent instability of wind speed may jeopardize the safety and operation of wind power generation, consequently affecting the power dispatch efficiency in power systems. Therefore, accurate short-term wind speed prediction can provide valuable information to solve the wind power grid connection problem. For this reason, the optimization of feedforward (FF) neural networks using an improved flower pollination algorithm is proposed. First of all, the empirical mode decomposition method is devoted to decompose the wind speed sequence into components of different frequencies for decreasing the volatility of the wind speed sequence. Secondly, a back propagation neural network is integrated with the improved flower pollination algorithm to predict the changing trend of each decomposed component. Finally, the predicted values of each component can get into an overlay combination process and achieve the purpose of accurate prediction of wind speed. Compared with major existing neural network models, the performance tests confirm that the average absolute error using the proposed algorithm can be reduced up to 3.67%.

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On the Pivotal Role of Artificial Intelligence Toward the Evolution of Smart Grids

Refaat¹,

Abu‐Rub²

2021

Smart Grid and Enabling Technologies

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Long-term load forecasting: models based on MARS, ANN and LR methods

Cited by 106 publications

References 31 publications

Methods and Models for Electric Load Forecasting: A Comprehensive Review

Methods and Models for Electric Load Forecasting: A Comprehensive Review

Optimization of Feedforward Neural Networks Using an Improved Flower Pollination Algorithm for Short-Term Wind Speed Prediction

On the Pivotal Role of Artificial Intelligence Toward the Evolution of Smart Grids

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