Applying Machine Learning Concepts to Enhance the Smart Grid Engineering Process The expansion of renewable energy sources, as an effort to reduce global warming and to guarantee a sustainable energy supply, forces the electrical energy systems into enhanced complexity through new requirements, actors, technological approaches or business models. This complexity is also noticed in the smart grid engineering process, resulting in increasing effort and costs. By applying machine learning concepts on the engineering process it is possible to decrease the workeffort and minimize tedious and error prone manual tasks. This work introduces three machine learning concepts and shows how they can improve the smart grid engineering process by applying a clustering approach to give recommendations of standards that are useful for the developed use case. According to their implementationfeasibility an evaluation based on the stateoftheart is pursued. Furthermore, a tool prototype indicates current and future application possibilities of machine learning in the smart grid engineering process.

ShortTerm Electricity Load and Price Forecasting using Enhanced KNN In this paper, we introduced a new enhanced technique, to resolve the issue of electricity price and load forecasting. In Smart Grids (SGs) Price and load forecasting is the major issue. Framework of enhanced technique comprises of classification and feature engineering. Feature engineering comprises of feature selection and feature extraction. Decision Tree Regression (DTR) is used for feature selection. Recursive Feature Elimination (RFE) is used for feature selection which eliminates the redundancy of features. The second step of feature engineering, feature extraction, is done using Singular Value Decomposition (SVD), which reduces the dimensionality of features. Last step is to predict the load and forecast. For forecasting electricity load and price, two existing techniques, KNearest Neighbors (KNN) and MultiLayer Perceptron (MLP), and a newly proposed technique known as Enhanced KNN (EKNN) is being used. The proposed technique outperforms than MLP and KNN in terms of accuracy. KNN is working on nonparametric method which is used for classification and regression.