Improvement of grey prediction models and their usage for energy demand forecasting

2018 ◽  
Vol 34 (4) ◽  
pp. 2679-2688 ◽  
Author(s):  
Beyzanur Cayir Ervural ◽  
Bilal Ervural
Keyword(s):  
Energy Demand ◽  
Prediction Models ◽  
Demand Forecasting ◽  
Grey Prediction ◽  
Energy Demand Forecasting

scholarly journals Interval Grey Prediction Models with Forecast Combination for Energy Demand Forecasting

Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 960
Author(s):  
Peng Jiang ◽  
Yi-Chung Hu ◽  
Wenbao Wang ◽  
Hang Jiang ◽  
Geng Wu
Keyword(s):  
Regression Analysis ◽  
Energy Demand ◽  
Prediction Models ◽  
Time Series Data ◽  
Demand Forecasting ◽  
Series Data ◽  
Grey Prediction ◽  
Combined Model ◽  
Forecasting Accuracy ◽  
Energy Demand Forecasting

Time series data for decision problems such as energy demand forecasting are often derived from uncertain assessments, and do not meet any statistical assumptions. The interval grey number becomes an appropriate representation for an uncertain and imprecise observation. In order to obtain nonlinear interval grey numbers with better forecasting accuracy, this study proposes a combined model by fusing interval grey numbers estimated by neural networks (NNs) and the grey prediction models. The proposed model first uses interval regression analysis using NNs to estimate interval grey numbers for a real valued sequence; and then a grey residual modification model is constructed using the upper and lower wrapping sequences obtained by NNs. It turns out that two different kinds of interval grey numbers can be estimated by nonlinear interval regression analysis. Forecasting accuracy on real data sequences was then examined by the best non-fuzzy performance values of the combined model. The proposed combined model performed well compared with the other interval grey prediction models considered.

scholarly journals Urban integrated energy demand forecasting method considering multi-station fusion

2021 ◽  
Vol 651 (2) ◽  
pp. 022084
Author(s):  
Haoyu Wu ◽  
Jiaxin Ma ◽  
Chunyan Zhang ◽  
Hua Zhou ◽  
Shimin Bian ◽  
...  
Keyword(s):  
Energy Demand ◽  
Demand Forecasting ◽  
Forecasting Method ◽  
Energy Demand Forecasting

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%.

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