Data Analytics for Short Term Price and Load Forecasting in Smart Grids using Enhanced Recurrent Neural Network

Short-Term Electricity Load Forecasting (STELF) through Data Analytics (DA) is an emerging and active research area. Forecasting about electricity load and price provides future trends and patterns of consumption. There is a loss in generation and use of electricity. So, multiple strategies are used to solve the aforementioned problems. Day-ahead electricity price and load forecasting are beneficial for both suppliers and consumers. In this paper, Deep Learning (DL) and data mining techniques are used for electricity load and price forecasting. XG-Boost (XGB), Decision Tree (DT), Recursive Feature Elimination (RFE) and Random Forest (RF) are used for feature selection and feature extraction. Enhanced Convolutional Neural Network (ECNN) and Enhanced Support Vector Regression (ESVR) are used as classifiers. Grid Search (GS) is used for tuning of the parameters of classifiers to increase their performance. The risk of over-fitting is mitigated by adding multiple layers in ECNN. Finally, the proposed models are compared with different benchmark schemes for stability analysis. The performance metrics MSE, RMSE, MAE, and MAPE are used to evaluate the performance of the proposed models. The experimental results show that the proposed models outperformed other benchmark schemes. ECNN performed well with threshold 0.08 for load forecasting. While ESVR performed better with threshold value 0.15 for price forecasting. ECNN achieved almost 2% better accuracy than CNN. Furthermore, ESVR achieved almost 1% better accuracy than the existing scheme (SVR).

Short-term electricity price and load forecasting in isolated power grids based on composite neural network and gravitational search optimization algorithm

In a deregulated power industry, accurate short term load forecasting (STLF) and price forecasting (STPF) is a key issue in daily power market. The load forecasting helps in unit commitment as well as in economic scheduling of the generators. The price forecasting helps an electric utility to make important decisions like generation of electric power, bidding for generation, price switching and infrastructure development. Price forecasting is very much useful for energy suppliers, ISOs and other participants in electric generation, transmission and distribution. This paper presents a hybrid approach for the STLF and STPF. The time series data pertaining to load / price is decomposed into various decomposition levels by the use of Wavelet Transform (WT) and each level obtained by this process is predicted using Artificial Neural Network (ANN). The performance of the proposed hybrid model is validated using New Delhi load data and Ontario electricity price data.

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 day-ahead 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.

In this paper, an accurate electricity load and price forecasting model has been proposed, which consists of feature engineering and classification. To remove irrelevant features, Decision Tree (DT) and Recursive Feature Elimination (RFE) are used. Features are extracted through Mutual Information (MI) after removing uncertainty. In order to attain accurate electricity load and price forecasting, Enhanced Logistic Regression (ELR) classifier is proposed. Simulation results testify that accuracy of ELR is better than Logistic Regression (LR) and MultiLayer Percepton (MLP). ELR beats LR and MLP by 0.26% and 7.287% in load forecasting, whereas, it outperforms LR and MLP in price forecasting by 1.413% and 3.057%, respectively. Smart* dataset is used, which contains the data of residential sector of Western Massachusetts. Prediction performance is evaluated by using Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE).

Accurate system marginal price and load forecasts play a pivotal role in economic power dispatch, system reliability and planning. Price forecasting helps electricity buyers and sellers in an energy market to make effective decisions when preparing their bids and making bilateral contracts. Despite considerable research work in this domain, load and price forecasting still remains to be a complicated task. Various uncertain elements contribute to electricity price and demand volatility, such as changes in weather conditions, outages of large power plants, fuel cost, complex bidding strategies and transmission congestion in the power system. Thus, to deal with such difficulties, we propose a novel hybrid deep learning method based upon bidirectional long short-term memory (BiLSTM) and a multi-head self-attention mechanisms that can accurately forecast locational marginal price (LMP) and system load on a day-ahead basis. Additionally, ensemble empirical mode decomposition (EEMD), a data-driven algorithm, is used for the extraction of hidden features from the load and price time series. Besides that, an intuitive understanding of how the proposed model works under the hood is demonstrated using different interpretability use cases. The performance of the presented method is compared with existing well-known techniques applied for short-term electricity load and price forecast in a comprehensive manner. The proposed method produces considerably accurate results in comparison to existing benchmarks.

Accurate load and price forecasting is one of the crucial stage in Smart Grid (SG). An efficient load and price forecasting is required to minimize the large difference among power generation and consumption. Accurate selection and extraction of meaningful features from data are challenging. In this paper, New York Independent System Operator (NYISO) six months' load and price data is used for forecasting. Decision Tree (DT) is used for feature selection and Recursive Feature Elimination (REF) technique is used for feature extraction. REF technique is used to remove redundancy from selected features. After feature extraction, two classifiers are used for forecasting. One classifier is Support Vector Machine (SVM) and other classifier is K-Nearest Neighbor (KNN). These classifiers have different parameters with some default values. Week ahead load and price forecasting is performed in this work. Accuracy of modified SVM is 89.5984% and modified KNN is 89.8605% is achieved for load forecasting. For price, accuracy of modified SVM is 88.2740% and modified KNN is 85.5999%.

In this paper, month-ahead electricity load and price forecasting is done to achieve accuracy. The data of electricity load is taken from the Smart Meter (SM) in London. Electricity load data of five months is taken from one block SM along with the weather data. Data Analytics (DA) techniques are used in the paper for month-ahead electricity load and price prediction. In this paper, forecasting is done in multiple stages. At first stage, feature extraction and selection is performed to make data suitable for efficient forecasting and to reduce complexity of data. After that, regression techniques are used for prediction. Singular Value Decomposition (SVD) is used for feature extraction afterwards; feature selection is done in two-stages, by using Random Forest (RF) and Recursive Feature Elimination (RFE). For electricity load and price forecasting Logistic Regression (LR), Support Vector Regression (SVR) is used. Moreover forecasting is done by the proposed technique Enhanced Support Vector Regression (EnSVR), which is modified from SVR. Simulation results show that the proposed system gives more accuracy in load and price prediction.

ESAENARX and DE-RELM: Novel schemes for big data predictive analytics of electricity load and price

In a modern and dynamic electricity market, ensuring reliable, sustainable and efficient electricity distribution is a pillar of primary importance for grid operation. The high penetration of renewable energy sources and the formation of competitive prices for utilities play a critical role in the wider economic development. Electricity load and price forecasting have been a key focus of researchers in the last decade due to the substantial economic implications for both producers, aggregators and end consumers. Many forecasting techniques and methods have emerged during this period. This paper conducts a extensive and analytical review of the prevailing load and electricity price forecasting methods in the context of the modern wholesale electricity market. The study is separated into seven main sections. The first section provides the key challenges and the main contributions of this study. The second section delves into the workings of the electricity market, providing a detailed analysis of the three markets that have evolved, their functions and the key factors influencing overall market dynamics. In the third section, the main methodologies of electricity load and price forecasting approaches are analyzed in detail. The fourth section offers a comprehensive review of the existing literature focusing on load forecasting, highlighting various methodologies, models and their applications in this field. This section emphasizes the advances that have been made in all categories of forecasting models and their practical application in different market scenarios. The fifth section focuses on electricity price forecasting studies, summarizing important research papers investigating various modeling approaches. The sixth section constitutes a fundamental discussion and comparison between the load- and price-focused studies that are analyzed. Finally, by examining both traditional and cutting-edge forecasting methods, this review identifies key trends, challenges and future directions in the field. Overall, this paper aims to provide an in-depth analysis leading to the understanding of the state-of-the-art models in load and price forecasting and to be an important resource for researchers and professionals in the energy industry. Based on the research conducted, there is an increasing trend in the use of artificial intelligence models in recent years, due to the flexibility and adaptability they offer for big datasets, compared to traditional models. The combination of models, such as ensemble methods, gives us very promising results.

With the recent developments in energy sector, the pricing of electricity is now governed by the spot market where a variety of market mechanisms are effective. After the new legislation of market liberalization in Turkey, competition-based on hourly price has received a growing interest in the energy market, which necessitated generators and electric utility companies to add new dimensions to their scope of operation: short-term load and price forecasting. The field has several opportunities though not free from challenges. The dynamic behavior of the market price has caused the electric load to become variable and non-stationary. Furthermore, the number of nodes, in which the load must be predicted, is not constant anymore and can no longer be estimated by experts alone. In this competitive scenario, statistical forecasting methods that can automatically and accurately process thousands of data samples are essential. The purpose of this study is to demonstrate the importance of short-term load forecasting, how it has received a growing interest in Turkey and to propose an artificial neural network that can forecast the short term electricity load. Through detailed performance evaluations, we demonstrate that our forecasting method is capable of predicting the hourly load accurately.

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.

Neural networks approach to forecast several hour ahead electricity prices and loads in deregulated market

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.

With the emergence of smart power grid and distributed generation technologies in recent years, there is need to introduce new advanced models for forecasting. Electricity load and price forecasts are two primary factors needed in a deregulated power industry. The performances of the demand response programs are likely to be deteriorated in the absence of accurate load and price forecasting. Electricity generation companies, system operators, and consumers are highly reliant on the accuracy of the forecasting models. However, historical prices from the financial market, weekly price/load information, historical loads and day type are some of the explanatory factors that affect the accuracy of the forecasting. In this paper, a neural network (NN) model that considers different influential factors as feedback to the model is presented. This model is implemented with historical data from the ISO New England. It is observed during experiments that price forecasting is more complicated and hence less accurate than the load forecasting.