Multiple regression analysis of performance parameters of a binary cycle geothermal power plant

Karadaş, Murat; Çelik, Hakan; Serpen, Umran; Toksoy, Macit

doi:10.1016/j.geothermics.2014.11.003

Cited by 25 publications

(9 citation statements)

References 23 publications

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“…This relationship can be expressed in function form, such as Markov chains [4], regression analysis [9,10], exponential smoothing method [11], Kalman filtering method [12], ARMA model [13] and so on. Among them, the ARMA (p, q) model, used as the common statistical model, has high accuracy of analysis and prediction for stationary time series, however, due to the impact of the uncertain natural climate, wind energy has obvious trends, diversity and periodicity, which show non-stationary time series.…”

Section: Introductionmentioning

confidence: 99%

A Multi Time Scale Wind Power Forecasting Model of a Chaotic Echo State Network Based on a Hybrid Algorithm of Particle Swarm Optimization and Tabu Search

Niu

et al. 2015

Energies

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Abstract:The uncertainty and regularity of wind power generation are caused by wind resources' intermittent and randomness. Such volatility brings severe challenges to the wind power grid. The requirements for ultrashort-term and short-term wind power forecasting with high prediction accuracy of the model used, have great significance for reducing the phenomenon of abandoned wind power , optimizing the conventional power generation plan, adjusting the maintenance schedule and developing real-time monitoring systems. Therefore, accurate forecasting of wind power generation is important in electric load forecasting. The echo state network (ESN) is a new recurrent neural network composed of input, hidden layer and output layers. It can approximate well the nonlinear system and achieves great results in nonlinear chaotic time series forecasting. Besides, the ESN is simpler and less computationally demanding than the traditional neural network training, which provides more accurate training results. Aiming at addressing the disadvantages of standard ESN, this paper has made some improvements. Combined with the complementary advantages of particle swarm optimization and tabu search, the generalization of ESN is improved. To verify the validity and applicability of this method, case studies of multitime scale forecasting of wind power output are carried out to reconstruct the chaotic time series of the actual wind power generation data in a certain region to predict wind power generation. Meanwhile, the influence of seasonal factors on wind power is taken into consideration. Compared with the classical ESN and the conventional Back Propagation (BP) neural network, the results verify the superiority of the proposed method.

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Section: Introductionmentioning

confidence: 99%

A Multi Time Scale Wind Power Forecasting Model of a Chaotic Echo State Network Based on a Hybrid Algorithm of Particle Swarm Optimization and Tabu Search

Niu

et al. 2015

Energies

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“…The statistical method does not consider the process of physical changes between wind speeds. It uses historical data to construct models corresponding to wind speed or wind power generation and constructs statistical models through parameter estimation or functions such as the Markov chain [15], regression analysis [16], the exponential smoothing method [17], the Kalman filtering method [18], the ARMA [19] model, etc. Among them, the ARMA (p,q) model, which is a commonly-used statistical model, presents high-precision analysis.…”

Section: Introductionmentioning

confidence: 99%

Short Term Wind Power Prediction Based on Data Regression and Enhanced Support Vector Machine

Hong

Huang

et al. 2020

Energies

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This paper presents a short-term wind power forecasting model for the next day based on historical marine weather and corresponding wind power output data. Due the large amount of historical marine weather and wind power data, we divided the data into clusters using the data regression (DR) algorithm to get meaningful training data, so as to reduce the number of modeling data and improve the efficiency of computing. The regression model was constructed based on the principle of the least squares support vector machine (LSSVM). We carried out wind speed forecasting for one hour and one day and used the correlation between marine wind speed and the corresponding wind power regression model to realize an indirect wind power forecasting model. Proper parameter settings for LSSVM are important to ensure its efficiency and accuracy. In this paper, we used an enhanced bee swarm optimization (EBSO) to perform the parameter optimization for LSSVM, which not only improved the forecast model availability, but also improved the forecasting accuracy.

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“…The maximum thermal efficiency was found to be related to the binary cycle with R123 as the working fluid and was calculated to be 7.65%. Karadas et al (2015) were conducted a regression analysis of 7.35 MW existing binary geothermal power plant using actual plant data to assess the plant performance. According to their analyses, since 2009, the plant performance was started to decline with 270 kW electricity generation capacities.…”

Section: Introductionmentioning

confidence: 99%

Düşük Enerji Seviyeli Kaynakla Çalışan Afyon Jeotermal Güç Santralinin Performans Değerlendirilmesi

Yılmaz

2020

Konya Journal of Engineering Sciences

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Exergoeconomic performance evaluation of 2700 kW binary cycle design geothermal power plant in western Turkey is conducted using actual plant operating data, and potential improvements are identified. Afyon Geothermal Power Plant is thermodynamically modeled in a computer environment using current working parameters in a comprehensive way for the use of geothermal energy in electricity generation. To simulate the plant during thermodynamic conditions with exergoeconomic analysis, the Engineering Equation Solver (EES) software is used. Geothermal water temperature and mass flow rate of the plant are 110°C, and 150 kg/s, respectively. Energy and exergy efficiencies of the plant are calculated as 10.4%, and 29.7%. The potential annual revenue of geothermal electricity is calculated to be 2,880,277 $/yr. The exergetic cost of the electricity from the plant is calculated as 0.0233 $/kWh, respectively.

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Multiple regression analysis of performance parameters of a binary cycle geothermal power plant

Cited by 25 publications

References 23 publications

A Multi Time Scale Wind Power Forecasting Model of a Chaotic Echo State Network Based on a Hybrid Algorithm of Particle Swarm Optimization and Tabu Search

A Multi Time Scale Wind Power Forecasting Model of a Chaotic Echo State Network Based on a Hybrid Algorithm of Particle Swarm Optimization and Tabu Search

Short Term Wind Power Prediction Based on Data Regression and Enhanced Support Vector Machine

Düşük Enerji Seviyeli Kaynakla Çalışan Afyon Jeotermal Güç Santralinin Performans Değerlendirilmesi

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