Overview[ edit ] Businesses face important decisions regarding what to sell, when to sell, to whom to sell, and for how much. Revenue management uses data-driven tactics and strategy to answer these questions in order to increase revenue. Today, the revenue management practitioner must be analytical and detail oriented, yet capable of thinking strategically and managing the relationship with sales.
This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In the forecasting process, EEMD is adopted to make the original wind speed data decomposed into intrinsic mode functions IMFs and one residual firstly.
Then, partial autocorrelation function PACF is applied to identify the correlation between the corresponding decomposed components. Subsequently, the MKLSSVM using multikernel function of radial basis function RBF and polynomial Poly kernel function by weight coefficient is exploited as core forecasting engine Forecasting optimization and objective function make the short-term wind speed prediction.
In the end, these respective decomposed subseries forecasting results are combined into the final forecasting values by aggregate calculation. Introduction Owing to the abundant, renewable, and economical characteristics, the exploitation and utilization technique of renewable wind energy have attracted extensive attention of the scientific researchers.
Wind energy has been considered as an effective way to address the global energy demands and eliminate green-house gas emissions [ 1 ]. In the past few years, wind energy has experienced fast growth worldwide.
Genetic algorithms constitute a class of search, adaptation, and optimization techniques based on the principles of natural evolution. Genetic algorithms were developed by Holland. Other evolutionary algorithms include evolution strategies, evolutionary programming, classifier systems, and genetic programming. Forecasting is the planning tool to predict the future outcomes based on historical data and experience, knowledge of the management. It is very important for the company for developing new products or product line in the marketplace. Optimization and Objective Function. 4 11 Spreadsheet Example 2 REVIEW 1. A mathematical programming problem is one that seeks to maximize an objective function subject to constraints. If both the objective function and the constraints are linear, the problem is referred to as a linear programming problem.
World Wind Energy Association reports that the total installed wind turbine capacity of the top 10 countries by the end of has approximately amounted to However, for the high fluctuation and nonlinear and uncontrollable nature of wind speed, the integration of power system with large capacity of wind power has brought new challenges to the operation security and reliability of power system and the management of wind farms.
Accurate short-term wind power output forecasting has been considered as one of the most economical and effective approaches to eliminate these problems; therefore, wind speed forecasting is a fundamental task in the routine operation management of wind farms [ 2 — 4 ]. Over the past decades, many methods and models, mainly including physical model, statistical model, and artificial intelligent method, are widely applied to predict the short-term wind speed [ 56 ].
Physical model is generally applied in the large-term wind speed forecasting by usage of detailed meteorological data and environmental information, while statistical models are constructed commonly for short-term wind speed forecasting by revealing explicitly the linear relationship among the wind speed time series [ 78 ].
Different from physical models and statistical methods, the artificial intelligent methods can tackle nonlinear problems better, thus, they are the most popular and extensive approaches to apply in the short-term wind speed forecasting.
As stated in [ 5 ], the single artificial intelligent model cannot work well when applied in wind speed forecasting in that wind speed exhibits high nonlinearity. Wind speed forecasting by the single model using directly the raw wind speed data without disposal is easily subjected from large errors; hence, multiscale decomposition or denoising processing techniques are utilized to preprocess wind data, and intelligent algorithms are used to tune the parameters in the forecasting engine.
For example, Liu at al. These hybrid forecasting models discussed above improve the prediction performance mainly by integration of the individual advantages of signal preprocessing technique and optimization algorithm and artificial intelligent model. Among these data preprocessing-based techniques as discussed and analyzed above, WT has sensitivity in the choice of threshold and the figuration of its wavelet basis should be determined beforehand, while EMD is sensitive to noise and suffers from mode mixing problems [ 619 ].
EEMD method can eliminate the drawbacks of the decomposition approaches to some extent. EEMD is an empirical and self-adaptive signal processing approach which is widely used to analyze the nonlinear and nonstationary signal so that we use EEMD to decompose and analyze the original wind speed data in this study.
LSSVM, proposed by Suykens [ 20 ], is an improved version of SVM, which lowers calculation complexity by translating convex quadratic programming problems into solving linear equations [ 21 ].
LSSVM can exhibit some advantages in solving small samples, nonlinearity, and pattern recognition with excellent generalization ability [ 22 ] and has been successfully applied in time series-based wind speed forecasting [ 102123 ], and therefore LSSVM algorithm is adopted as the core forecasting engine for short-term wind speed forecasting.
Even though these signal decomposition based models have obtained good forecasting results, Wang et al. To address this problem, the feature selection method is utilized widely [ 2526 ]. In [ 8 ], Kullback-Leibler divergence-based and energy-based feature selections were exploited to identify the illusive components caused by the decomposed method EEMD.
In [ 27 ], Salcedo-Sanz developed a hybrid model of physical model and ELM, where coral reefs optimization algorithm CRO was utilized as feature selection to select the useful meteorological predictive information from the output of the physical approach.Swarm-based algorithms emerged as a powerful family of optimization techniques, inspired by the collective behavior of social animals.
In particle swarm optimization (PSO) the set of candidate solutions to the optimization problem is defined as a swarm of particles which may flow through the parameter space defining trajectories which are driven by their own and neighbors' best performances.
4 11 Spreadsheet Example 2 REVIEW 1. A mathematical programming problem is one that seeks to maximize an objective function subject to constraints. If both the objective function and the constraints are linear, the problem is referred to as a linear programming problem. There is currently a lot of buzz about using machine learning (ML) techniques for predicting the future state of a supply chain (demand forecasting being the most popular use case).
Genetic algorithms constitute a class of search, adaptation, and optimization techniques based on the principles of natural evolution. Genetic algorithms were developed by Holland. Other evolutionary algorithms include evolution strategies, evolutionary programming, classifier systems, and genetic programming.
1. Introduction. In their analysis of research in time series forecasting, covering the period – and summarizing over papers, De Gooijer and Hyndman conclude that the use of prediction intervals and densities, or probabilistic forecasting, has become much more common over the years, as ‘practitioners have come to understand the limitations of point forecasts’.
Theses and Dissertations topics related to Supply Chain Management, Procurement Management, Inventory Management, and Distribution Management.