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AbstractDue to strong increase of solar power generation, the predictions of incoming solar energy are acquiring more importance. Photovoltaic and solar thermal are the main sources of electricity generation from solar energy. In the case of solar thermal energy plants with storage energy system, its management and operation need reliable predictions of solar irradiance with the same temporal resolution as the temporal capacity of the back-up system. These plants can work like a conventional power plant and compete in the energy stock market avoiding intermittence in electricity production. This work presents a comparisons of statistical models based on time series applied to predict half daily values of global solar irradiance with a temporal horizon of 3 days. Half daily values consist of accumulated hourly global solar irradiance from solar raise to solar noon and from noon until dawn for each day. The dataset of ground solar radiation used belongs to stations of Spanish National Weather Service (AEMet). The models tested are autoregressive, neural networks and fuzzy logic models. Due to the fact that half daily solar irradiance time series is non-stationary, it has been necessary to transform it to two new stationary variables (clearness index and lost component) which are used as input of the predictive models. Improvement in terms of RMSD of the models essayed is compared against the model based on persistence. The validation process shows that all models essayed improve persistence. The best approach to forecast half daily values of solar irradiance is neural network models with lost component as input, except Lerida station where models based on clearness index have less uncertainty because this magnitude has a linear behaviour and it is easier to simulate by models.Article Outline1. Introduction 1.1. Solar thermal energy with storage energy system1.2. Solar irradiance predictions1.3. Solar irradiance predictions with applications in solar thermal power plants2. Solar radiation dataset3. Methodology 3.1. Autoregressive models (AR)3.2. Neural networks (NN)3.3. Adaptative-network-based fuzzy inference system (ANFIS)4. Results and discussions 4.1. Models results4.2. Final model selection5. ConclusionAcknowledgementsReferences95A hybrid optimization approach for distribution capacitor allocation considering varying load conditionsOriginal Research ArticleInternational Journal of Electrical Power & Energy Systems, Volume 31, Issue 10, November-December 2009, Pages 589-595Alireza Seifi, Mohammad Reza HesamzadehClose preview| Purchase PDF (357 K) | Related articles|Related reference work articles AbstractAbstract | Figures/TablesFigures/Tables | ReferencesReferences AbstractThis work presents a new algorithm based on a combination of fuzzy (FUZ), Forward Update (FWD), and Genetic Algorithm (GA) approaches for capacitor allocation in distribution feeders. The problem formulation considers three distinct objectives related to total cost of energy loss and total cost of capacitors including the purchase and installation costs and one term related to total cost of produced power under peak load condition. The novel formulation is a multi-objective and non-differentiable optimization problem. The proposed methodology of this article uses an iterative optimization technique based on Forward Update approach which is embedded in a Genetic Algorithm framework. The fuzzy reasoning supported by the fuzzy set theory is used for sitting of capacitors and the GA is employed for finding the optimum shape of membership functions. The proposed method has been implemented in a software package and its effectiveness has been verified through a 9-bus radial distribution feeder along with a 34-bus radial distribution feeder for the sake of conclusions supports. A comparison has been done among the proposed method of this paper and similar methods in other research works that shows the effectiveness of the proposed method of this paper for solving optimum capacitor planning problem.Article Outline1. Introduction2. Mathematical model of the problem3. Proposed hybrid optimization approach for capacitor allocation in radial distribution networks 3.1. Fuzzy modelling3.2. Constant load condition3.3. Varying load condition 3.3.1. Method 1 fuzzy product approach3.3.2. Method 2 effective load modelling approach4. Case study 4.1. Constant load condition (9-bus system)4.2. Varying load condition (34-bus system) 4.2.1. Fuzzy product method4.2.2. The effective load model5. ConclusionReferences96The urbanization of wildlife management: Social science, conflict, and decision makingOriginal Research ArticleUrban Forestry & Urban Greening, Volume 1, Issue 3, 2003, Pages 171-183Michael E. Patterson, Jessica M. Montag, Daniel R. WilliamsShow preview| Purchase PDF (183 K) | Related articles|Related reference work articles 97Offshoring manufacturing: Implications for engineering jobs and education: A survey and case studyOriginal Research ArticleRobotics and Computer-Integrated Manufacturing, Volume 22, Issues 5-6, October-December 2006, Pages 576-587Bopaya Bidanda, Ozlem Arisoy, Larry J. 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