Short-Term Wind Power Prediction Based on Improved Chicken Algorithm Optimization Support Vector Machine

Fu, Chao; Li, Guoquan; Lin, Kuo-Ping; Zhang, Huijuan

doi:10.3390/su11020512

Cited by 40 publications

(21 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, a hybrid model for electricity load and price forecasting based on a combination of Stochastic Gradient Descent and SVM that shows improved prediction accuracy is the subject of [46]. Other approaches use deep belief network to forecast the hourly load of the power grid [47] and combine chicken swarm optimization algorithm with SVM to make predictions on short-term wind power and improve the stability of power system operation reporting a better convergence and accuracy compared with other bio-inspired models [48].…”

Section: Related Workmentioning

confidence: 99%

Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs

et al. 2020

View full text Add to dashboard Cite

In this paper, we address the problem of the efficient and sustainable operation of data centers (DCs) from the perspective of their optimal integration with the local energy grid through active participation in demand response (DR) programs. For DCs’ successful participation in such programs and for minimizing the risks for their core business processes, their energy demand and potential flexibility must be accurately forecasted in advance. Therefore, in this paper, we propose an energy prediction model that uses a genetic heuristic to determine the optimal ensemble of a set of neural network prediction models to minimize the prediction error and the uncertainty concerning DR participation. The model considers short term time horizons (i.e., day-ahead and 4-h-ahead refinements) and different aspects such as the energy demand and potential energy flexibility (the latter being defined in relation with the baseline energy consumption). The obtained results, considering the hardware characteristics as well as the historical energy consumption data of a medium scale DC, show that the genetic-based heuristic improves the energy demand prediction accuracy while the intra-day prediction refinements further reduce the day-ahead prediction error. In relation to flexibility, the prediction of both above and below baseline energy flexibility curves provides good results for the mean absolute percentage error (MAPE), which is just above 6%, allowing for safe DC participation in DR programs.

show abstract

Section: Related Workmentioning

confidence: 99%

Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Compared with the classical optimizer such as particle swarm optimizer and ant swarm optimizer, it has stronger convergence ability and robustness. The CSO optimizer follows these rules (Al Shayokh and Shin, 2017;Fu et al, 2019;Tiana et al, 2017):…”

Section: Chicken Swarm Optimizer (Cso)mentioning

confidence: 99%

Power load forecasting in energy system based on improved extreme learning machine

Chen

Wun

et al. 2020

Energy Exploration & Exploitation

View full text Add to dashboard Cite

Through the accurate prediction of power load, the start and stop of generating units in the power grid can be arranged economically and reasonably. The safety and stability of power grid operation can be maintained. First, chicken swarm optimizer based on nonlinear dynamic convergence factor (NCSO) optimizer is proposed based on chicken swarm optimizer (CSO) optimizer. In NCSO optimizer, nonlinear dynamic inertia weight and levy mutation strategy are introduced. Compared with CSO optimizer, the convergence speed and effect of NCSO optimizer are obviously improved. Second, the random parameters of extreme learning machine (ELM) model are optimized by NCSO optimizer, and NCSOELM model is established to predict the power load. Finally, the NCSO optimization extreme learning machine (NCSOELM) model is used to predict the power load, and compared with back propagation (BP), support vector machine (SVM) and CSO optimization extreme learning machine (CSOELM) model. The experimental results show that the fitting accuracy of NCSOELM model is high, and the determination coefficient r2 is above 90%. And the root mean square error value of the NCSOELM model is 0.87, 0.41, and 0.25 smaller than the root mean square error values of the support vector machine, BP, and CSOELM models, respectively. Experiments show that the model proposed in this study has high fitting effect and low prediction error, which is of positive significance for the realization of economic and safe operation of energy system.

show abstract

“…In the optimization process, the arrangement arbitrarily strolls in its neighbourhood with a probability dictated by Metropolis rule while the system temperature diminishes gradually; when the annealing temperature is shutting zero, the arrangement remains at the worldwide best arrangement in a high probability. [11][12][13] The application of SA in optimization problem is formulated as an NLP problem, expressing the objective function and constraint functions in term of the specified independent variables. The objective function is expressed as : Optimize f(x) System ANFIS SA Algorithm Such that 'x' exists within the n-dimensional feasible region D: X ı D, where D = {x | x >=0, g i (x) <=0, hi (x) = 0, i=1 to n} In the above equations, f(x), g i (x) are real valued scalar functions and vector x comprises the n principal variables for which the optimization is to be performed.…”

Section: Simulated Annealing Algorithmmentioning

confidence: 99%

Short Term Solar Irradiation Forecasting using ANFIS and Simulated Annealing ANFIS

Srivastava¹

2019

IJRASET

View full text Add to dashboard Cite

The physical interaction between the hydrological variables (such as solar irradiation, evapotranspiration) with solar irradiance is highly nonlinear, stochastic, and complex. The solar power prediction models can be divided into two groups, namely, i) physical and ii) system theoretic. The main drawback of the physical model is the complexity of the models, which increases with increase in model parameters. Further, the development of these models is based on understanding of the physical processes in the system. On the other hand, the system theoretic model is based on data driven techniques, where the mapping or learning of the models is done through data itself. Here, the understanding of the physical process in model building is avoided to a large extent. For this SA and ANFIS, both Artificial Intelligence techniques are used to a great extent. Two years data from March 2017 to March 2019 of Sultanpur district has been taken for the forecasting of solar irradiance using the two techniques and the result is compared by calculating the RMSE values.

show abstract

Short-Term Wind Power Prediction Based on Improved Chicken Algorithm Optimization Support Vector Machine

Cited by 40 publications

References 28 publications

Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs

Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs

Power load forecasting in energy system based on improved extreme learning machine

Short Term Solar Irradiation Forecasting using ANFIS and Simulated Annealing ANFIS

Contact Info

Product

Resources

About