Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting

Singh, Shailendra; Yassine, Abdulsalam

doi:10.3390/en11020452

Cited by 183 publications

(75 citation statements)

References 58 publications

(68 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In Reference [8], behavioural analytics are performed using Bayesian network and Multi Layer Perceptron (MLP). A number of experiments were performed using the data obtainedfrom the smart meters.…”

Section: Electricity Load Forecastingmentioning

confidence: 99%

“…The pseudocode of RFE is given in Algorithm 1. 6 Selecting the most important variables from S(i) 7 Preprocessing the data 8 Tuning the model using predictions 9 Calculating the performance 10 Recalculation of the rankings 11 end 12 Establish the performance profile using S(i) 13 Determine the number of important variables 14 Use the model corresponding to optimized S(i) 15 End…”

Section: Recursive Feature Elimination (Rfe)mentioning

confidence: 99%

“…The pseudocode of Relief-F is discussed in Algorithm 2 [36]. Find k-nearest hits, Hj 6 end 7 for (All other classes C != class (Ri)) do 8 Find k nearest misses, Mj(C) 9 end 10 for (A = 1 to a) do 11 Update weight of all attributes using Equation (1)…”

Section: Relief-fmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

Section: Electricity Load Forecastingmentioning

confidence: 99%

Section: Recursive Feature Elimination (Rfe)mentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Most recently, techniques from the field of artificial intelligence have been evaluated with success. Thus, Lago et al [9] used various deep learning approaches and compared them to traditional algorithms/forecasting methods, Singh & Yassine and Gajowniczek & Ząbkowski [10,11] applied big data mining and machine learning algorithms to load forecasting and Wang et al [12] applied a deep learning algorithm based on the assembly approach to forecast probabilistic wind power production using quantile regression. In references [13,14], the authors developed hybrid models combining ARIMA, kernel-based extreme learning machine and neural networks to forecast day and week ahead electricity prices.…”

Section: Introductionmentioning

confidence: 99%

A Trading-Based Evaluation of Density Forecasts in a Real-Time Electricity Market

2018

View full text Add to dashboard Cite

This paper applies a multi-factor, stochastic latent moment model to predicting the imbalance volumes in the Austrian zone of the German/Austrian electricity market. This provides a density forecast whose shape is determined by the flexible skew-t distribution, the first three moments of which are estimated as linear functions of lagged imbalance and forecast errors for load, wind and solar production. The evaluation of this density predictor is compared to an expected value obtained from OLS regression model, using the same regressors, through an out-of-sample backtest of a flexible generator seeking to optimize its imbalance positions on the intraday market. This research contributes to forecasting methodology and imbalance prediction, and most significantly it provides a case study in the evaluation of density forecasts through decision-making performance. The main finding is that the use of the density forecasts substantially increased trading profitability and reduced risk compared to the more conventional use of mean value regressions.

show abstract

“…First, electricity demand forecasting has been addressed by using deep learning [1], ensemble learning [2] and the functional state space model [3]. Analogously, data from the UK and Canada were analyzed in [4], generating accurate forecasts. Unsupervised techniques have also been used to discover relevant patterns within consumption time series.…”

mentioning

confidence: 99%

Data Science and Big Data in Energy Forecasting

2018

View full text Add to dashboard Cite

This editorial summarizes the performance of the special issue entitled Data Science and Big Data in Energy Forecasting, which was published at MDPI's Energies journal. The special issue took place in 2017 and accepted a total of 13 papers from 7 different countries. Electrical, solar and wind energy forecasting were the most analyzed topics, introducing new methods with applications of utmost relevance.

show abstract

Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting

Cited by 183 publications

References 58 publications

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

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

A Trading-Based Evaluation of Density Forecasts in a Real-Time Electricity Market

Data Science and Big Data in Energy Forecasting

Contact Info

Product

Resources

About