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Electricity Load and Price Forecasting Using Machine Learning Algorithms in Smart Grid: A Survey

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Abstract

Conventional grid moves towards Smart Grid (SG). In conventional grids, electricity is wasted in generation-transmissions-distribution, and communication is in one direction only. SG is introduced to solve prior issues. In SG, there are no restrictions, and communication is bi-directional. Electricity forecasting plays a significant role in SG to enhance operational cost and efficient management. Load and price forecasting gives future trends. In literature many data-driven methods have been discussed for price and load forecasting. The objective of this paper is to focus on literature related to price and load forecasting in last four years. The author classifies each paper in terms of its problems and solutions. Additionally, the comparison of each proposed technique regarding performance are presented in this paper. Lastly, papers limitations and future challenges are discussed.

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... In other words, at any given time, the forecasting system for an hour later predicts a quantity based on the existing data [73][74][75][76][77][78][79][80][81][82][83]. This model is not very efficient, and little has been done for the price [84][85][86][87][88][89][90]. In the short-term or day-ahead forecasting method, the forecast is usually done for the next day. ...
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Electricity price forecasting is a significant part of smart grid because it makes smart grid cost efficient. Nevertheless, existing methods for price forecasting may be difficult to handle with huge price data in the grid, since the redundancy from feature selection cannot be averted and an integrated infrastructure is also lacked for coordinating the procedures in electricity price forecasting. To solve such a problem, a novel electricity price forecasting model is developed. Specifically, three modules are integrated in the proposed model. First, by merging of Random Forest (RF) and Relief-F algorithm, we propose a hybrid feature selector based on Grey Correlation Analysis (GCA) to eliminate the feature redundancy. Second, an integration of Kernel function and Principle Component Analysis (KPCA) is used in feature extraction process to realize the dimensionality reduction. Finally, to forecast price classification, we put forward a differential evolution (DE) based Support Vector Machine (SVM) classifier. Our proposed electricity price forecasting model is realized via these three parts. Numerical results show that our proposal has superior performance than other methods.
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In real word it is quite meaningful to forecast the day-ahead electricity load for an area, which is beneficial to reduction of electricity waste and rational arrangement of electric generator units. The deployment of various sensors strongly pushes this forecasting research into a "big data" era for a huge amount of information has been accumulated. Meanwhile the prosperous development of deep learning (DL) theory provides powerful tools to handle massive data and often outperforms conventional machine learning methods in many traditional fields. Inspired by these, we propose a deep learning based model which firstly refines features by stacked denoising auto-encoders (SDAs) from history electricity load data and related temperature parameters, subsequently trains a support vector regression (SVR) model to forecast the day-ahead total electricity load. The most significant contribution of this heterogeneous deep model is that the abstract features extracted by SADs from original electricity load data are proven to describe and forecast the load tendency more accurately with lower errors. We evaluate this proposed model by comparing with plain SVR and artificial neural networks (ANNs) models, and the experimental results validate its performance improvements.
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Electricity prices have rather complex features such as high volatility, high frequency, nonlinearity, mean reversion and non-stationarity that make forecasting very difficult. However, accurate electricity price forecasting is essential to market traders, retailers, and generation companies. To improve prediction accuracy using each model’s unique features, this paper proposes a hybrid approach that combines the wavelet transform, the kernel extreme learning machine (KELM) based on self-adapting particle swarm optimization and an auto regressive moving average (ARMA). Self-adaptive particle swarm optimization (SAPSO) is adopted to search for the optimal kernel parameters of the KELM. After testing the wavelet decomposition components, stationary series as new input sets are predicted by the ARMA model and non-stationary series are predicted by the SAPSO-KELM model. The performance of the proposed method is evaluated by using electricity price data from the Pennsylvania-New Jersey-Maryland (PJM), Australian and Spanish markets. The experimental results show that the developed method has more accurate prediction, better generality and practicability than individual methods and other hybrid methods.
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Big data mining, analysis, and forecasting always play a vital role in modern economic and industrial fields. Thus, how to select an optimization model to improve the forecasting accuracy of electricity price is not only an extremely challenging problem but also a concerned problem for different participants in electricity market due to our society becoming heavily reliant on electricity. Many researchers developed hybrid models through the use of optimization methods, classical statistical models, artificial intelligence approaches and de-noising methods. However, few researchers aim to select reasonable samples and determine appropriate features when forecasting electricity price. Based on the Index of Bad Samples Matrix (IBSM), a novel method to dynamically confirm bad training samples, and the Optimization Algorithm (OA), DCANN and Updated DCANN are proposed in this paper for forecasting the day-ahead electricity price. This model is a hybrid system of supervised and unsupervised learning and creatively applies the idea of deleting bad samples and searching quality inputs to develop and learn, which is unlike BPANN, RBFN, SVM and LSSVM. Numerical results show that the proposed model is not only able to approximate the actual electricity price (normal or high volatility) but also an effective tool for h-step-ahead forecasting (h is less than 10) compared to benchmarks.
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Day-ahead electricity prices are generally used as reference prices for decisions done in energy trading, e.g. purchase and sale strategies are typically based on the day-ahead spot prices. Therefore, wellperforming forecast methods for day-ahead electricity prices are essential for energy traders and supply companies. In this paper, a methodology based on artificial neuronal networks (ANN) is presented to forecast electricity prices. As the performance of an ANN forecast model depends on appropriate input parameter sets, the focus is set on the selection and preparation of fundamental data that has a noticeable impact on electricity prices. This is done with the help of different cluster algorithms, but also by comparing the results of the pre-selected model configurations in combination with different input parameter settings. After the determination of the optimal input parameters, affecting day-ahead electricity prices, and wellperforming ANN configuration, the developed ANN model is applied for in-sample and out-of-sample analyses. The results show that the overall methodology leads to well-fitting electricity price forecasts, whereas forecast errors are as low as or even lower than other forecast models for electricity prices known from the literature.
Electricity load and price forecasting using Jaya-Long Short Term Memory (JLSTM) in smart grids
  • R Khalid
  • N Javaid
  • FA Al-zahrani
  • K Aurangzeb
  • EUH Qazi
  • T Ashfaq
Application of big data and machine learning in smart grid, and associated security concerns: a review
  • E Hossain
  • I Khan
  • F Un-Noor
  • SS Sikander
  • MSH Sunny