scholarly journals Hybrid Forecasting Model for Short-Term Electricity Market Prices with Renewable Integration

2018 ◽  
Vol 11 (1) ◽  
pp. 57 ◽  
Author(s):  
Gerardo Osório ◽  
Mohamed Lotfi ◽  
Miadreza Shafie-khah ◽  
Vasco Campos ◽  
João Catalão
Keyword(s):  
Electricity Market ◽  
Wavelet Transforms ◽  
Fuzzy Inference ◽  
Electricity Sector ◽  
Forecasting Model ◽  
Probabilistic Forecasting ◽  
Short Term ◽  
Market Prices ◽  
Inference System ◽  
Hybrid Particle Swarm Optimization

In recent years, there have been notable commitments and obligations by the electricity sector for more sustainable generation and delivery processes to reduce the environmental footprint. However, there is still a long way to go to achieve necessary sustainability goals while ensuring standards of robustness and the quality of power grids. One of the main challenges hindering this progress are uncertainties and stochasticity associated with the electricity sector and especially renewable generation. In this paradigm shift, forecasting tools are indispensable, and their utilization can significantly improve system operation and minimize costs associated with all related activities. Thus, forecasting tools have an essential key role in all decision-making stages. In this work, a hybrid probabilistic forecasting model (HPFM) was developed for short-term electricity market prices (EMP) combining wavelet transforms (WT), hybrid particle swarm optimization (DEEPSO), adaptive neuro-fuzzy inference system (ANFIS), and Monte Carlo simulation (MCS). The proposed hybrid probabilistic forecasting model (HPFM) was tested and validated with real data from the Spanish and Pennsylvania-New Jersey-Maryland (PJM) markets. The proposed model exhibited favorable results and performance in comparison with previously published work considering electricity market prices (EMP) data, which is notable.

scholarly journals Hour-Ahead Photovoltaic Output Forecasting Using Wavelet-ANFIS

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2438
Author(s):  
Chao-Rong Chen ◽  
Faouzi Brice Ouedraogo ◽  
Yu-Ming Chang ◽  
Devita Ayu Larasati ◽  
Shih-Wei Tan
Keyword(s):  
Output Power ◽  
Power Output ◽  
Wavelet Transforms ◽  
Fuzzy Inference ◽  
Model Performance ◽  
Integrated System ◽  
Discrete Wavelet ◽  
Short Term ◽  
Correct Operation ◽  
Inference System

The operational challenge of a photovoltaic (PV) integrated system is the uncertainty (irregularity) of the future power output. The integration and correct operation can be carried out with accurate forecasting of the PV output power. A distinct artificial intelligence method was employed in the present study to forecast the PV output power and investigate the accuracy using endogenous data. Discrete wavelet transforms were used to decompose PV output power into approximate and detailed components. The decomposed PV output was fed into an adaptive neuro-fuzzy inference system (ANFIS) input model to forecast the short-term PV power output. Various wavelet mother functions were also investigated, including Haar, Daubechies, Coiflets, and Symlets. The proposed model performance was highly correlated to the input set and wavelet mother function. The statistical performance of the wavelet-ANFIS was found to have better efficiency compared with the ANFIS and ANN models. In addition, wavelet-ANFIS coif2 and sym4 offer the best precision among all the studied models. The result highlights that the combination of wavelet decomposition and the ANFIS model can be a helpful tool for accurate short-term PV output forecasting and yield better efficiency and performance than the conventional model.

scholarly journals Ensemble Recurrent Neural Network Based Probabilistic Wind Speed Forecasting Approach

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 1958 ◽  
Author(s):  
Lilin Cheng ◽  
Haixiang Zang ◽  
Tao Ding ◽  
Rong Sun ◽  
Miaomiao Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Recurrent Neural Network ◽  
Fuzzy Inference ◽  
Ensemble Model ◽  
Probabilistic Forecasting ◽  
Inference System ◽  
Wind Speed Forecasting ◽  
Wind Power Integration ◽  
Neuro Fuzzy

Wind energy is a commonly utilized renewable energy source, due to its merits of extensive distribution and rich reserves. However, as wind speed fluctuates violently and uncertainly at all times, wind power integration may affect the security and stability of power system. In this study, we propose an ensemble model for probabilistic wind speed forecasting. It consists of wavelet threshold denoising (WTD), recurrent neural network (RNN) and adaptive neuro fuzzy inference system (ANFIS). Firstly, WTD smooths the wind speed series in order to better capture its variation trend. Secondly, RNNs with different architectures are trained on the denoising datasets, operating as submodels for point wind speed forecasting. Thirdly, ANFIS is innovatively established as the top layer of the entire ensemble model to compute the final point prediction result, in order to take full advantages of a limited number of deeplearningbased submodels. Lastly, variances are obtained from submodels and then prediction intervals of probabilistic forecasting can be calculated, where the variances inventively consist of modeling and forecasting uncertainties. The proposed ensemble model is established and verified on less than one-hour-ahead ultra-short-term wind speed forecasting. We compare it with other soft computing models. The results indicate the feasibility and superiority of the proposed model in both point and probabilistic wind speed forecasting.

scholarly journals PERANCANGAN MODEL PENDUKUNG KEPUTUSAN PENGADAAN BARANG DENGAN MENGGUNAKAN FUZZY INFERENCE SYSTEM

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Viera Astry ◽  
Dadang Surjasa ◽  
Dedy Sugiarto
Keyword(s):  
Fuzzy Inference System ◽  
Consumer Satisfaction ◽  
Fuzzy Inference ◽  
Expert Knowledge ◽  
Arima Model ◽  
Forecasting Model ◽  
Demand Forecast ◽  
Validation Process ◽  
Inference System ◽  
Verification Process

<p>Alleriea is a small medium enterprises engaged in the field of providing souvenirs. To increase consumer satisfaction, the company should be able to fullfill consumer demand, The decisions support system in this study is using Fuzzy Inference System with Mamdani type as intuitive and very suitable to be given expert knowledge. This model was designed using MATLAB software and as input will be used to predict the number of requests, the speed of supply and stock condition.<br />The predicted number of demand are made by using forecasting methods by selecting a forecasting model with the smallest MSE value. Based on the comparison of the value of MSE on the ARIMA model and winter, forecasting results obtained by the method of Winter has the smallest MSE value.<br />The verification process is done by looking at the forecasting model with the smallest MSE, the validation process is done to test the normality of residual data. The verification process on fuzzy inference systems is done by testing whether the rules given leave in accordance with the desired output. The validation process using a combination of testing Extreme Test uses a combination of extreme in any condition. The result of this paper is a procurement decision support model using fuzzy inference system which influenced by the demand forecast, stock condition and speed of supply. Designed models have been verified and validated.</p>

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