Short-Term Electric Load and Price Forecasting Using Enhanced Extreme Learning Machine Optimization in Smart Grids

Abstract

A Smart Grid (SG) is a modernized grid to provide efficient, reliable and economic energy to the consumers. Energy is the most important resource in the world. An efficient energy distribution is required as smart devices are increasing dramatically. The forecasting of electricity consumption is supposed to be a major constituent to enhance the performance of SG. Various learning algorithms have been proposed to solve the forecasting problem. The sole purpose of this work is to predict the price and load efficiently. The first technique is Enhanced Logistic Regression (ELR) and the second technique is Enhanced Recurrent Extreme Learning Machine (ERELM). ELR is an enhanced form of Logistic Regression (LR), whereas, ERELM optimizes weights and biases using a Grey Wolf Optimizer (GWO). Classification and Regression Tree (CART), Relief-F and Recursive Feature Elimination (RFE) are used for feature selection and extraction. On the basis of selected features, classification is performed using ELR. Cross validation is done for ERELM using Monte Carlo and K-Fold methods. The simulations are performed on two different datasets. The first dataset, i.e., UMass Electric Dataset is multi-variate while the second dataset, i.e., UCI Dataset is uni-variate. The first proposed model performed better with UMass Electric Dataset than UCI Dataset and the accuracy of second model is better with UCI than UMass. The prediction accuracy is analyzed on the basis of four different performance metrics: Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Square Error (MSE) and Root Mean Square Error (RMSE). The proposed techniques are then compared with four benchmark schemes. The comparison is done to verify the adaptivity of the proposed techniques. The simulation results show that the proposed techniques outperformed benchmark schemes. The proposed techniques efficiently increased the prediction accuracy of load and price. However, the computational time is increased in both scenarios. ELR achieved almost 5% better results than Convolutional Neural Network (CNN) and almost 3% than LR. While, ERELM achieved almost 6% better results than ELM and almost 5% than RELM. However, the computational time is almost 20% increased with ELR and 50% with ERELM. Scalability is also addressed for the proposed techniques using half-yearly and yearly datasets. Simulation results show that ELR gives 5% better results while, ERELM gives 6% better results when used for yearly dataset.

The Recent Development of Artificial Intelligence for Smart and Sustainable Energy Systems and Applications

“…[4] # Home energy management and ambient assisted living Non-intrusive load monitoring techniques [5] Non-intrusive load monitoring for energy disaggregation Genetic algorithm; support vector machine; multiple kernel learning [6] Optimizing residential energy consumption Bacterial foraging optimization; flower pollination [7] Non-intrusive load monitoring for energy disaggregation Long short-time memory and decision tree [8] Energy efficient coverage in wireless sensor network Distributed genetic algorithm [9] Estimation of load and price of electric grid Enhanced logistic regression; enhanced recurrent extreme learning machine; classification and regression tree; relief-F and recursive feature elimination [10] Detection of the insulators in power transmission and transformation inspection images Improved faster region-convolutional neural network [11] Non-intrusive load monitoring for energy disaggregation Concatenate convolutional neural network [12] Non-intrusive load monitoring for energy disaggregation Linear-chain conditional random fields [13] Prediction of the rheological properties of calcium chloride brine-based mud Artificial neural network [14] Estimation of Static Young's Modulus for sandstone formation Artificial neural network; self-adaptive differential evolution # Review article.…”

“…Predicting the load and price of the electric grid have been important tasks for utility in lowering the electricity cost and improving the service quality for end customers. A. Naz, M. U. Javed, N. Javaid, T. Saba, M. Alhussein, and K. Aurangzeb have addressed these topics via an article "Short-term electric load and price forecasting using enhanced extreme learning machine optimization in smart grids" [9]. Two-feature extraction approaches were adopted, namely classification and regression tree, relief-F, and recursive feature elimination.…”

Forecasting Agricultural Commodity Prices Using Model Selection Framework With Time Series Features and Forecast Horizons

“…Unlike traditional learning algorithms in feedforward neural network, where parameters are tuned iteratively, the Moore-Penrose generalized inverse is applied to determine the output weights in ELM [6], thus requiring little time for training. This advantage has been applied to classification tasks and regression tasks in numerous studies [27], [46], [47].…”

Model and Algorithm of Electricity Consumption Management for Household Consumers in the Republic of Tajikistan

“…As an example, from the point of view of territorial location, the ambient temperature in Dushanbe does not fall below -5℃ in winter, and in the Gorno-Badakhshan Autonomous Region with the center of Khorog, the ambient temperature in winter can reach -30℃, therefore, electricity consumption by household consumers in Khorog will be higher than in Khorog. The influence of temperature on power consumption has been sufficiently studied [8][9][10][11][12].…”