Daily peak electricity demand forecasting based on an adaptive hybrid two-stage methodology

Laouafi, Abderrezak; Mordjaoui, Mourad; Laouafi, Farida; Boukelia, Taqiy Eddine

doi:10.1016/j.ijepes.2015.11.046

Cited by 60 publications

(33 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Premise and consequent parameters will be identified for membership function (MF) and FIS by repeating the forward and backward passes. ANFIS is fuzzy Sugeno model put in the framework of adaptive systems to facilitate learning and adaption [7]. Such framework makes Fuzzy Logic Controller more systematic and less relying on expert knowledge [13].…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Optimization of Biogas Electrical Power Generation using Neuro-Fuzzy Controller

Oluwaseun¹,

Onyeamaechi

Sunday

2020

TMLAI

View full text Add to dashboard Cite

Biogas electrical power generation is a renewable energy which originated from biological materials. The technology design and model power system that predict and control the generation of biogas Electrical production. This research paper develops a Neuro-fuzzy controller model for generation of Biogas power production. A Neuro-fuzzy controller is design to the Biogas power system in order to improve the power quality delivery to the load. The set of 27 rules are written for proper training of biogas electrical data in the neural network. The training is used to control signal of the Biogas Power output of the system. The output of Neural Network unit is given as input to the de-fuzzification unit and the linguistic variables are converted back into the crisp form. Therefore the algorithm was designed to decide power supply to the load as to improve the performance of the biogas system using MATLAB/SIMULINK and Neuro-fuzzy model was developed for easy input of the data. The result shows that biogas electrical power output increased by 4.39kw, which is 54.8% increase when Neuro-fuzzy controller is incorporated. The improvement in the system is due to the training of input parameters of the biogas generated. The result obtained shows that there is Real Power improvement in Biogas system when Neuro-fuzzy is incorporated in the system model

show abstract

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Optimization of Biogas Electrical Power Generation using Neuro-Fuzzy Controller

Oluwaseun¹,

Onyeamaechi

Sunday

2020

TMLAI

View full text Add to dashboard Cite

show abstract

“…Processes with heavy-tailed distributions have instead been estimated by Bystrom (2005), Panagiotelis & Smith (2008) and Swider & Weber (2007). Other authors have coped with the issue of predicting price spikes which are particularly relevant for risk management (Laouafi et al, 2016). In this context, Christensen et al (2012) suggested a modified autoregressive conditional hazard model to predict price spikes in the Australian electricity market.…”

Section: Literature Reviewmentioning

confidence: 99%

The Influence of Renewables on Electricity Price Forecasting: A Robust Approach

Grossi

Nan

2018

SSRN Journal

View full text Add to dashboard Cite

In this paper a robust approach to modelling electricity spot prices is introduced. Differently from what has been recently done in the literature on electricity price forecasting, where the attention has been mainly drawn by the prediction of spikes, the focus of this contribution is on the robust estimation of nonlinear SETARX models (Self-Exciting Threshold Auto Regressive models with eXogenous regressors). In this way, parameters estimates are not, or very lightly, influenced by the presence of extreme observations and the large majority of prices, which are not spikes, could be better forecasted. A Monte Carlo study is carried out in order to select the best weighting function for Generalized M-estimators of SETAR processes. A robust procedure to select and estimate nonlinear processes for electricity prices is introduced, including robust tests for stationarity and nonlinearity and robust information criteria. The application of the procedure to the Italian electricity market reveals the forecasting superiority of the robust GM-estimator based on the polynomial weighting function respect to the non-robust Least Squares estimator. Finally, the introduction of external regressors in the robust estimation of SETARX processes contributes to the improvement of the forecasting ability of the model.

show abstract

“…In addition, with the development of signal processing research, researchers have invented some novel and effective denoising strategies and applied them to the data preprocessing of time series. For example, strategies such as the Wavelet Packet Transform (WPT) [58], Improved Empirical Mode Decomposition (IEMD) [59], and Ensemble Empirical Mode Decomposition (EEMD) [60] have been successfully employed in the field of electricity load forecasting to reduce the random disturbance of original data, thus obtaining a better forecasting performance.…”

Section: Introductionmentioning

confidence: 99%

Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia

Wang

Tang

et al. 2019

Energies

View full text Add to dashboard Cite

Electricity load forecasting plays an essential role in improving the management efficiency of power generation systems. A large number of load forecasting models aiming at promoting the forecasting effectiveness have been put forward in the past. However, many traditional models have no consideration for the significance of data preprocessing and the constraints of individual forecasting models. Moreover, most of them only focus on the forecasting accuracy but ignore the forecasting stability, resulting in nonoptimal performance in practical applications. This paper presents a novel hybrid model that combines an advanced data preprocessing strategy, a deep neural network, and an avant-garde multi-objective optimization algorithm, overcoming the defects of traditional models and thus improving the forecasting performance effectively. In order to evaluate the validity of the proposed hybrid model, the electricity load data sampled in 30-min intervals from Queensland, Australia are used as a case to study. The experiments show that the new proposed model is obviously superior to all other traditional models. Furthermore, it provides an effective technical forecasting means for smart grid management.

show abstract

Daily peak electricity demand forecasting based on an adaptive hybrid two-stage methodology

Cited by 60 publications

References 36 publications

Optimization of Biogas Electrical Power Generation using Neuro-Fuzzy Controller

Optimization of Biogas Electrical Power Generation using Neuro-Fuzzy Controller

The Influence of Renewables on Electricity Price Forecasting: A Robust Approach

Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia

Contact Info

Product

Resources

About