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.

DCNN and LDARFRFE Based ShortTerm Electricity Load and Price Forecasting In this paper, Deep Convolutional Neural Network (DCNN) is proposed for short term electricity load and price forecasting. Extracting useful information from data and then using that information for prediction is a challenging task. This paper presents a model consisting of two stages; feature engineering and prediction. Feature engineering comprises of Feature Extraction (FE) and Feature Selection (FS). For FS, this paper proposes a technique that is combination of Random Forest (RF) and Recursive Feature Elimination (RFE). The proposed technique is used for feature redundancy removal and dimensionality reduction. After finding the useful features DCNN is used for electricity price and load forecasting. DCNN performance is compared with Convolutional Neural Network (CNN) and Support Vector Classifier (SVC) models. Using the forecasting models dayahead and the week ahead forecasting is done for electricity price and load. To evaluate the CNN, SVC and DCNN models, real electricity market data is used. Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are used to evaluate the performance of the models. DCNN outperforms compared models by yielding lesser errors.