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Short-Term Electricity Load and Price Forecasting using Enhanced KNN

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Abstract

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, K-Nearest Neighbors (KNN) and Multi-Layer 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.
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... Alongside the improvement in computation capability of processors and Machine Learning (ML) and Deep Learning (DL) techniques in computer science, electrical engineering researchers find out the benefits of deploying ML and DL for demand forecasting. For example, references [10]- [12] developed K-Nearest Neighbors (KNN), Support Vector Regression (SVR), and Artificial Neural Network (ANN) as data-driven demand forecast models at New York state, Northeastern China, and Pecan street, respectively. A deep convolution neural network is presented in [13] for demand forecast in three Chinese cities. ...
... The loss function is defined in according with (10). In the training stage, the model is trained in a way in which the expected value of loss is minimized. ...
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Chapter
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