Current status of wind energy forecasting and a hybrid method for hourly predictions

Okumuş, İnci; Dinler, Ali

doi:10.1016/j.enconman.2016.06.053

Cited by 240 publications

(111 citation statements)

References 72 publications

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“…Timescale classification for methods of wind power forecasting vary in the literature. According to Chang [37], timescale methods of wind power forecasting can be divided into four categories, shown in Table 4: In Okumus and Dinler [38] it is possible to obtain more details about different time scales for wind speed prediction and its nomenclatures. They relied on several (current) references to present definitions for wind speed forecast horizon within the wind power generation sector, but it is important to note that in the literature there is no consensus on these scales, i.e., variations in the definitions are often found for different authors.…”

Section: Resultsmentioning

confidence: 99%

Innovative Hybrid Modeling of Wind Speed Prediction Involving Time-Series Models and Artificial Neural Networks

et al. 2018

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This work proposes hybrid models combining time-series models (using linear functions) and artificial intelligence (using a nonlinear function) that can be used to provide monthly mean wind speed predictions for the Brazilian northeast region. These might be useful for wind power generation; for example, they could acquire important information on how the local wind potential can be usable for a possible wind power plant through understanding future wind speed values. To create the proposed hybrid models, it was necessary to set the wind speed variable as a dependent variable of exogenous variables (i.e., pressure, temperature, and precipitation). Thus, it was possible to consider the meteorological characteristics of the study regions. It is possible to verify the hybrid models' efficiency in providing perfect adjustments to the observed data. This statement is based on the low values found in the error statistical analysis, i.e., an error of approximately 5.0% and a Nash-Sutcliffe coefficient near to 0.96. These results were certainly important in predicting the wind speed time-series, which was similar to the observed wind speed time-series profile. Great similarities of maximums and minimums between the series were evident and showed the capacity of the models to represent the seasonality characteristics.

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Section: Resultsmentioning

confidence: 99%

Innovative Hybrid Modeling of Wind Speed Prediction Involving Time-Series Models and Artificial Neural Networks

et al. 2018

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“…Volkans used the ARIMA (Autoregressive distributed lag Model) to study the energy demand of Turkey [28]; Org used the f-ARIMA model to forecast wind speed [29]; Farahbakhsh applied the residential energy model when studying the residential energy consumption of Canada [30]. Some scholars forecasted the different energy demand based on a single model [31][32][33][34]; Okumus et al forecasted wind energy power by combining adaptive neural-fuzzy interference system (ANFIS) model with the artificial neural network (ANN), and the obtained error was below 4% while they all exceeded 5% in previous related study [35]. After trying several arithmetic simulations, Yu discovered that Shanghai natural gas short-term load could be forecasted more accurately with relatively less iterations by optimizing Genetic algorithm model (GA Model) and the improving BP neural network [36].…”

Section: Study Of the Applications Of Energy Prediction Modelsmentioning

confidence: 99%

Predicting Coal Consumption in South Africa Based on Linear (Metabolic Grey Model), Nonlinear (Non-Linear Grey Model), and Combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model) Models

et al. 2018

Sustainability

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South Africa's coal consumption accounts for 69.6% of the total energy consumption of South Africa, and this represents more than 88% of African coal consumption, taking the first place in Africa. Thus, predicting the coal demand is necessary, in order to ensure the supply and demand balance of energy, reduce carbon emissions and promote a sustainable development of economy and society. In this study, the linear (Metabolic Grey

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“…To obtain the wind power distribution, a linear approximation equation that established the relationship between the wind speed and the wind power is presented in (14):…”

Section: Wind Distributionmentioning

confidence: 99%

Electrical Energy Forecasting and Optimal Allocation of ESS in a Hybrid Wind-Diesel Power System

et al. 2017

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Due to the increasingly serious energy crisis and environmental pollution problem, traditional fossil energy is gradually being replaced by renewable energy in recent years. However, the introduction of renewable energy into power systems will lead to large voltage fluctuations and high capital costs. To solve these problems, an energy storage system (ESS) is employed into a power system to reduce total costs and greenhouse gas emissions. Hence, this paper proposes a two-stage method based on a back-propagation neural network (BPNN) and hybrid multi-objective particle swarm optimization (HMOPSO) to determine the optimal placements and sizes of ESSs in a transmission system. Owing to the uncertainties of renewable energy, a BPNN is utilized to forecast the outputs of the wind power and load demand based on historic data in the city of Madison, USA. Furthermore, power-voltage (P-V) sensitivity analysis is conducted in this paper to improve the converge speed of the proposed algorithm, and continuous wind distribution is discretized by a three-point estimation method. The Institute of Electrical and Electronic Engineers (IEEE) 30-bus system is adopted to perform case studies. The simulation results of each case clearly demonstrate the necessity for optimal storage allocation and the efficiency of the proposed method.

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Current status of wind energy forecasting and a hybrid method for hourly predictions

Cited by 240 publications

References 72 publications

Innovative Hybrid Modeling of Wind Speed Prediction Involving Time-Series Models and Artificial Neural Networks

Innovative Hybrid Modeling of Wind Speed Prediction Involving Time-Series Models and Artificial Neural Networks

Predicting Coal Consumption in South Africa Based on Linear (Metabolic Grey Model), Nonlinear (Non-Linear Grey Model), and Combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model) Models

Electrical Energy Forecasting and Optimal Allocation of ESS in a Hybrid Wind-Diesel Power System

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