scholarly journals Usage of the Pareto Fronts as a Tool to Select Data in the Forecasting Process—A Short-Term Electric Energy Demand Forecasting Case

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3204
Author(s):  
Michał Sabat ◽  
Dariusz Baczyński
Keyword(s):  
Energy Demand ◽  
Demand Forecasting ◽  
Electric Energy ◽  
Forecasting Model ◽  
Short Term ◽  
Complex Evaluation ◽  
Proposed Model ◽  
Energy Demand Forecasting ◽  
Pareto Fronts ◽  
Naive Approach

Transmission, distribution, and micro-grid system operators are struggling with the increasing number of renewables and the changing nature of energy demand. This necessitates the use of prognostic methods based on ever shorter time series. This study depicted an attempt to develop an appropriate method by introducing a novel forecasting model based on the idea to use the Pareto fronts as a tool to select data in the forecasting process. The proposed model was implemented to forecast short-term electric energy demand in Poland using historical hourly demand values from Polish TSO. The study rather intended on implementing the range of different approaches—scenarios of Pareto fronts usage than on a complex evaluation of the obtained results. However, performance of proposed models was compared with a few benchmark forecasting models, including naïve approach, SARIMAX, kNN, and regression. For two scenarios, it has outperformed all other models by minimum 7.7%.

Monthly electric energy demand forecasting with neural networks and Fourier series

2008 ◽  
Vol 49 (11) ◽  
pp. 3135-3142 ◽  
Author(s):  
E. González-Romera ◽  
M.A. Jaramillo-Morán ◽  
D. Carmona-Fernández
Keyword(s):  
Neural Networks ◽  
Fourier Series ◽  
Energy Demand ◽  
Demand Forecasting ◽  
Electric Energy ◽  
Energy Demand Forecasting

scholarly journals Electricity Load and Price Forecasting Using Jaya-Long Short Term Memory (JLSTM) in Smart Grids

Entropy ◽  
2019 ◽  
Vol 22 (1) ◽  
pp. 10 ◽  
Author(s):  
Rabiya Khalid ◽  
Nadeem Javaid ◽  
Fahad A. Al-zahrani ◽  
Khursheed Aurangzeb ◽  
Emad-ul-Haq Qazi ◽  
...  
Keyword(s):  
Big Data ◽  
Short Term Memory ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Term Memory ◽  
Proposed Model ◽  
Multiple Variables ◽  
Long Short Term Memory

In the smart grid (SG) environment, consumers are enabled to alter electricity consumption patterns in response to electricity prices and incentives. This results in prices that may differ from the initial price pattern. Electricity price and demand forecasting play a vital role in the reliability and sustainability of SG. Forecasting using big data has become a new hot research topic as a massive amount of data is being generated and stored in the SG environment. Electricity users, having advanced knowledge of prices and demand of electricity, can manage their load efficiently. In this paper, a recurrent neural network (RNN), long short term memory (LSTM), is used for electricity price and demand forecasting using big data. Researchers are working actively to propose new models of forecasting. These models contain a single input variable as well as multiple variables. From the literature, we observed that the use of multiple variables enhances the forecasting accuracy. Hence, our proposed model uses multiple variables as input and forecasts the future values of electricity demand and price. The hyperparameters of this algorithm are tuned using the Jaya optimization algorithm to improve the forecasting ability and increase the training mechanism of the model. Parameter tuning is necessary because the performance of a forecasting model depends on the values of these parameters. Selection of inappropriate values can result in inaccurate forecasting. So, integration of an optimization method improves the forecasting accuracy with minimum user efforts. For efficient forecasting, data is preprocessed and cleaned from missing values and outliers, using the z-score method. Furthermore, data is normalized before forecasting. The forecasting accuracy of the proposed model is evaluated using the root mean square error (RMSE) and mean absolute error (MAE). For a fair comparison, the proposed forecasting model is compared with univariate LSTM and support vector machine (SVM). The values of the performance metrics depict that the proposed model has higher accuracy than SVM and univariate LSTM.

Monthly Electric Energy Demand Forecasting Based on Trend Extraction

2006 ◽  
Vol 21 (4) ◽  
pp. 1946-1953 ◽  
Author(s):  
E. Gonzalez-Romera ◽  
M.A. Jaramillo-Moran ◽  
D. Carmona-Fernandez
Keyword(s):  
Energy Demand ◽  
Demand Forecasting ◽  
Electric Energy ◽  
Trend Extraction ◽  
Energy Demand Forecasting

Long And Short Term Energy Demand Forecasting Using Xgboost Models

2021 ◽  
Author(s):  
Jose Robles ◽  
Freedy Sotelo-Valer ◽  
Johnny Nahui-Ortiz ◽  
Jorge Lopez-Cordova
Keyword(s):  
Energy Demand ◽  
Demand Forecasting ◽  
Short Term ◽  
Energy Demand Forecasting ◽  
Term Energy
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