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The paper presents the planning (sizing) of a hybrid islanded system containing only renewable sources including wind turbines, photovoltaics, and bio‐waste energy units for the simultaneous supply of electrical and thermal energy. The mentioned renewable sources are used to supply electrical energy. The bio‐waste unit (BEU) is equipped with combined electrical and thermal technology. It is used along with heat pumps to supply thermal energy. Electrical and thermal storage are employed to make the renewable power output as close as possible to the demand level. Electric storage can be either stationary (e.g., battery) or mobile (e.g., electric vehicles), but thermal storage is as stationary type of storage. In the following, the proposed scheme minimizes construction and maintenance costs imposed by power sources, storage devices, and power electronic converters, and expected storage degradation cost. This plan is bound to the model of operation of sources, storage devices, and power electronic converters. In this model, renewables are the main source of power to supply consumers, where storage is adopted to increase the generation level to make it as close as possible to the demand curve. The uncertain parameters are imposed by the load, renewable phenomena, and mobile storage parameters. To model these uncertainties, stochastic optimization based on unscented transformation is used. Finally, the findings of the paper demonstrate that the suggested scheme succeeds in the economic planning of the system with a simultaneous supply of electrical and thermal energy. The BEU equipped with a combined electricity and heat system along with thermal storage and a heat pump to supply thermal load besides providing electric energy can reduce the planning cost by 2.9% compared with the case with only electrical energy. Also, the mobile storage presence in the hybrid system can reduce the number of stationary storage devices, which alone results in a 7.7% decrease in the planning cost of the hybrid system. [ABSTRACT FROM AUTHOR]

Data pre-processing is one of the crucial phases of data mining that enhances the efficiency of data mining techniques. One of the most important operations performed on data pre-processing is missing values imputation in incomplete datasets. This research presents a new imputation technique using K-means and samples weighting mechanism based on Grey relation (KWGI). The Grey-based K-means algorithm applicable to all samples of incomplete datasets clusters the similar samples, then an appropriate kernel function generates appropriate weights based on the Grey relation. The missing values estimation of the incomplete samples is done based on the weighted mean to reduce the impact of outlier and vague samples. In both clustering and imputation steps, a penalty mechanism has been considered to reduce the similarity of ambiguous samples with a high number of missing values, and consequently, increase the accuracy of clustering and imputation. The KWGI method has been applied on nine natural datasets with eight state-of-the-art and commonly used methods, namely CMIWD, KNNI, HotDeck, MeanI, KmeanI, RKmeanI, ICKmeanI, and FKMI. The imputation results are evaluated by the Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) criteria. In this study, the missing values are generated at two levels, namely sample and value, and the results are discussed in a wide range of missingness from low rate to high rate. Experimental results of the t-test show that the proposed method performs significantly better than all the other compared methods. [ABSTRACT FROM AUTHOR]