Medium term load forecasting using ANFIS predictor

Dragomir, Otilia Elena; Dragomir, Florin; Gouriveau, Rafael; Minca, Eugénia

doi:10.1109/med.2010.5547727

Cited by 14 publications

(6 citation statements)

References 25 publications

(22 reference statements)

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“…In addition to ANN, several methods have been used to forecast electricity demand; e.g., Gaussian Process Model [31], Support Vector Machine (SVM) [32], Mixed Lazy Learning (MLL) [33], Adaptive Neuro Fuzzy Inference System (ANFIS) [34] and Fuzzy Logic (FL) [35].…”

Section: Introductionmentioning

confidence: 99%

A Smart Forecasting Approach to District Energy Management

2017

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Abstract:This study presents a model for district-level electricity demand forecasting using a set of Artificial Neural Networks (ANNs) (parallel ANNs) based on current energy loads and social parameters such as occupancy. A comprehensive sensitivity analysis is conducted to select the inputs of the ANN by considering external weather conditions, occupancy type, main income providers' employment status and related variables for the fuel poverty index. Moreover, a detailed parameter tuning is conducted using various configurations for each individual ANN. The study also demonstrates the strength of the parallel ANN models in different seasons of the years. In the proposed district level energy forecasting model, the training and testing stages of parallel ANNs utilise dataset of a group of six buildings. The aim of each individual ANN is to predict electricity consumption and the aggregated demand in sub-hourly time-steps. The inputs of each ANN are determined using Principal Component Analysis (PCA) and Multiple Regression Analysis (MRA) methods. The accuracy and consistency of ANN predictions are evaluated using Pearson coefficient and average percentage error, and against four seasons: winter, spring, summer, and autumn. The lowest prediction error for the aggregated demand is about 4.51% for winter season and the largest prediction error is found as 8.82% for spring season. The results demonstrate that peak demand can be predicted successfully, and utilised to forecast and provide demand-side flexibility to the aggregators for effective management of district energy systems.

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Section: Introductionmentioning

confidence: 99%

A Smart Forecasting Approach to District Energy Management

2017

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“…ANFIS is applied on a database obtain from an experimental photovoltaic amphitheater of minimum dimensions (0.4 kV/10 kW), located in the east-center region of Romania, more precisely in the city of Targoviste [14]. The used data base has 296 data points tpy ptq , u ptq , t " 1, .…”

Section: Simulation Conditions and Strategymentioning

confidence: 99%

“…The efficiency of using Neural Networks (NN) in the area of energy forecasting was demonstrated in our previous works [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Inference Systems as a Strategy for Predicting and Controling the Energy Produced from Renewable Sources

Dragomir

Ştefan

et al. 2015

Energies

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Abstract:The challenge for our paper consists in controlling the performance of the future state of a microgrid with energy produced from renewable energy sources. The added value of this proposal consists in identifying the most used criteria, related to each modeling step, able to lead us to an optimal neural network forecasting tool. In order to underline the effects of users' decision making on the forecasting performance, in the second part of the article, two Adaptive Neuro-Fuzzy Inference System (ANFIS) models are tested and evaluated. Several scenarios are built by changing: the prediction time horizon (Scenario 1) and the shape of membership functions (Scenario 2).

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“…Time-series prediction is an active area of research due to the variety of applications in financial markets [66,67], weather forecasting [68][69][70], among others. Moreover, different researches have demonstrated that ANFIS is adapted for time-series prediction such as Mackey-Glass [58,60,71], Box-Jenkins [71], Duffing forced-oscillation system [60], wind speed and direction [70], energy market [47] or machinery degradation data [49,72].…”

Section: Prediction Of Time-seriesmentioning

confidence: 99%

“…Among these techniques, Adaptive Neuro-Fuzzy Inference Systems (ANFIS) are considered because they do not require complex mathematical models, they are fast and adaptive and the developed prediction tool can be implemented on-line, which is essential for PHM of fuel cell systems. Their principal drawback is that the performance of the predictions is highly depending in quantity and quality of data [46][47][48][49]. Approaches based on the use of experimental data to construct neuro-fuzzy inference systems have been used in a variety of applications such as medical [50][51][52], motors [53][54][55] and fuel cell diagnosis [56].…”

Section: Introductionmentioning

confidence: 99%

Proton exchange membrane fuel cell degradation prediction based on Adaptive Neuro-Fuzzy Inference Systems

Silva

Gouriveau

Jemeï

et al. 2014

International Journal of Hydrogen Energy

221

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This paper studies the prediction of the output voltage reduction caused by degradation during nominal operating condition of a PEM fuel cell stack. It proposes a methodology based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) which use as input the measures of the fuel cell output voltage during operation. The paper presents the architecture of the ANFIS and studies the selection of its parameters. As the output voltage cannot be represented as a periodical signal, the paper proposes to predict its temporal variation which is then used to construct the prediction of the output voltage. The paper also proposes to split this signal in two components: normal operation and external perturbations. The second component cannot be predicted and then it is not used to train the ANFIS. The performance of the prediction is evaluated on the output voltage of two fuel cells during a long term operation (1000 hours). Validation results suggest that the proposed technique is well adapted to predict degradation in fuel cell systems.

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Medium term load forecasting using ANFIS predictor

Cited by 14 publications

References 25 publications

A Smart Forecasting Approach to District Energy Management

A Smart Forecasting Approach to District Energy Management

Adaptive Neuro-Fuzzy Inference Systems as a Strategy for Predicting and Controling the Energy Produced from Renewable Sources

Proton exchange membrane fuel cell degradation prediction based on Adaptive Neuro-Fuzzy Inference Systems

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