Advanced fuzzy time series applied to short term load forecasting

Silva, Guilherme Costa; Silva, Joao L. R.; Lisboa, Adriano C.; Vieira, D.A.G.; Saldanha, R.R.

doi:10.1109/la-cci.2017.8285726

Cited by 13 publications

(10 citation statements)

References 20 publications

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“…Besides, these methods allow the forecaster to consider numerous exogenous factors such as temperature and humidity (Kuster et al, 2017). As reported in the literature, several researchers have investigated the performance of fuzzy logic on the electrical load forecasting efficiency and accuracy (Al-Kandari et al, 2004;Danladi et al, 2016;Faysal et al, 2019;Silva et al, 2017) and today fuzzy logic is widely used for load forecast. Rizwan et al (2012) investigated daily hourly load demand using historical load data as input to the fuzzy logic model.…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of Different Membership Function in Fuzzy Logic Based Short-Term Load Forecasting

Ibrahim¹,

Owonikoko²,

Abdulkarim³

et al. 2021

JST

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A mismatch between utility-scale electricity generation and demand often results in resources and energy wastage that needed to be minimized. Therefore, the utility company needs to be able to accurately forecast load demand as a guide for the planned generation. Short-term load forecast assists the utility company in projecting the future energy demand. The predicted load demand is used to plan ahead for the power to be generated, transmitted, and distributed and which is crucial to power system reliability and economics. Recently, various methods from statistical, artificial intelligence, and hybrid methods have been widely used for load forecasts with each having their merits and drawbacks. This paper investigates the application of the fuzzy logic technique for short-term load forecast of a day ahead load. The developed fuzzy logic model used time, temperature, and historical load data to forecast 24 hours load demand. The fuzzy models were based on both the trapezoidal and triangular membership function (MF) to investigate their accuracy and effectiveness for the load forecast. The obtained low Mean Absolute Percentage Error (MAPE), Mean Forecast Error (MFE), and Mean Absolute Deviation (MAD) values from the forecasted load results showed that both models are suitable for short-term load forecasting, however the trapezoidal MF showed better performance than the triangular MF.

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Section: Related Workmentioning

confidence: 99%

Performance Evaluation of Different Membership Function in Fuzzy Logic Based Short-Term Load Forecasting

Ibrahim¹,

Owonikoko²,

Abdulkarim³

et al. 2021

JST

View full text Add to dashboard Cite

show abstract

“…The literature has proposed many novel methods for short-term load forecasting like fuzzy [18], exponential smoothing [19], regression based [20], neural networks [21], and others. Moreover, every proposed model has incorporated some techniques.…”

Section: Related Workmentioning

confidence: 99%

Game Theoretical Demand Response Management and Short-Term Load Forecasting by Knowledge Based Systems on the basis of Priority Index

et al. 2018

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Demand Response Management (DRM) is considered one of the crucial aspects of the smart grid as it helps to lessen the production cost of electricity and utility bills. DRM becomes a fascinating research area when numerous utility companies are involved and their announced prices reflect consumer’s behavior. This paper discusses a Stackelberg game plan between consumers and utility companies for efficient energy management. For this purpose, analytical consequences (unique solution) for the Stackelberg equilibrium are derived. Besides this, this paper presents a distributed algorithm which converges for consumers and utilities. Moreover, different power consumption activities on the basis of time series are becoming a basic need for load prediction in smart grid. Load forecasting is taken as the significant concerns in the power systems and energy management with growing technology. The better precision of load forecasting minimizes the operational costs and enhances the scheduling of the power system. The literature has discussed different techniques for demand load forecasting like neural networks, fuzzy methods, Naïve Bayes, and regression based techniques. This paper presents a novel knowledge based system for short-term load forecasting. The algorithms of Affinity Propagation and Binary Firefly Algorithm are integrated in knowledge based system. Besides, the proposed system has minimum operational time as compared to other techniques used in the paper. Moreover, the precision of the proposed model is improved by a different priority index to select similar days. The similarity in climate and date proximity are considered all together in this index. Furthermore, the whole system is distributed in sub-systems (regions) to measure the consequences of temperature. Additionally, the predicted load of the entire system is evaluated by the combination of all predicted outcomes from all regions. The paper employs the proposed knowledge based system on real time data. The proposed scheme is compared with Deep Belief Network and Fuzzy Local Linear Model Tree in terms of accuracy and operational cost. In addition, the presented system outperforms other techniques used in the paper and also decreases the Mean Absolute Percentage Error (MAPE) on a yearly basis. Furthermore, the novel knowledge based system gives more efficient outcomes for demand load forecasting.

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“…The BPPN was implemented for inflation rate prediction [6], pollutant emissions prediction [7], and water simulation [8]. Fuzzy logic based methods were used to address uncertainty in time series data such as wind and wave climate [9], short-term electrical load [10], and longterm electrical load [11].…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithmised Neuro Fuzzy System for Forecasting the Online Journal Visitors

Mahmudy¹,

Wibawa²,

Sari³

et al. 2021

IJC

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Artificial Neural Network (ANN) is recognized as one of effective forecasting engines for various business fields. This approach fits well with non-linear data. In fact, it is a black box system with random weighting, which is hard to train. One way to improve its performance is by hybridizing ANN with other methods. In this paper, a hybrid approach, Genetic Algorithm-Neural Fuzzy System (GA-NFS) is proposed to predict the number of unique visitors of an online journal website. The neural network weight is precisely determined using GA. Afterwards, the best weight has been used for testing data and processed using Sugeno Fuzzy Inference System (FIS) for time-series forecasting. Based on experiment, GA-NFS have been produced accuracy with 0.989 of root mean square error (RMSE) that is lower than the RMSE of a common NFS (2,004). This may indicate that the GA based weighting is able to improve the NFS performance on forecasting the number of journal unique visitors.

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Advanced fuzzy time series applied to short term load forecasting

Cited by 13 publications

References 20 publications

Performance Evaluation of Different Membership Function in Fuzzy Logic Based Short-Term Load Forecasting

Performance Evaluation of Different Membership Function in Fuzzy Logic Based Short-Term Load Forecasting

Game Theoretical Demand Response Management and Short-Term Load Forecasting by Knowledge Based Systems on the basis of Priority Index

Genetic Algorithmised Neuro Fuzzy System for Forecasting the Online Journal Visitors

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