Monthly Electric Energy Consumption Forecasting Using Multiwindow Moving Average and Hybrid Growth Models
Monthly electric energy consumption forecasting is important for electricity production planning and electric power engineering decision making. Multiwindow moving average algorithm is proposed to decompose the monthly electric energy consumption time series into several periodic waves and a long-te...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2014/243171 |
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| _version_ | 1849691768555044864 |
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| author | Ming Meng Wei Shang Dongxiao Niu |
| author_facet | Ming Meng Wei Shang Dongxiao Niu |
| author_sort | Ming Meng |
| collection | DOAJ |
| description | Monthly electric energy consumption forecasting is important for electricity production planning and electric power engineering decision making. Multiwindow moving average algorithm is proposed to decompose the monthly electric energy consumption time series into several periodic waves and a long-term approximately exponential increasing trend. Radial basis function (RBF) artificial neural network (ANN) models are used to forecast the extracted periodic waves. A novel hybrid growth model, which includes a constant term, a linear term, and an exponential term, is proposed to forecast the extracted increasing trend. The forecasting results of the monthly electric energy consumption can be obtained by adding the forecasting values of each model. To test the performance by comparison, the proposed and other three models are used to forecast China's monthly electric energy consumption from January 2011 to December 2012. Results show that the proposed model exhibited the best performance in terms of mean absolute percentage error (MAPE) and maximal absolute percentage error (MaxAPE). |
| format | Article |
| id | doaj-art-b672d4394bf642979f148526bb538dbe |
| institution | DOAJ |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-b672d4394bf642979f148526bb538dbe2025-08-20T03:20:56ZengWileyJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/243171243171Monthly Electric Energy Consumption Forecasting Using Multiwindow Moving Average and Hybrid Growth ModelsMing Meng0Wei Shang1Dongxiao Niu2School of Economics and Management, North China Electric Power University, No. 619, Yonghua Street, Baoding, Hebei 071003, ChinaSchool of Economics, Hebei University, Baoding, Hebei 071002, ChinaSchool of Economics and Management, North China Electric Power University, No. 619, Yonghua Street, Baoding, Hebei 071003, ChinaMonthly electric energy consumption forecasting is important for electricity production planning and electric power engineering decision making. Multiwindow moving average algorithm is proposed to decompose the monthly electric energy consumption time series into several periodic waves and a long-term approximately exponential increasing trend. Radial basis function (RBF) artificial neural network (ANN) models are used to forecast the extracted periodic waves. A novel hybrid growth model, which includes a constant term, a linear term, and an exponential term, is proposed to forecast the extracted increasing trend. The forecasting results of the monthly electric energy consumption can be obtained by adding the forecasting values of each model. To test the performance by comparison, the proposed and other three models are used to forecast China's monthly electric energy consumption from January 2011 to December 2012. Results show that the proposed model exhibited the best performance in terms of mean absolute percentage error (MAPE) and maximal absolute percentage error (MaxAPE).http://dx.doi.org/10.1155/2014/243171 |
| spellingShingle | Ming Meng Wei Shang Dongxiao Niu Monthly Electric Energy Consumption Forecasting Using Multiwindow Moving Average and Hybrid Growth Models Journal of Applied Mathematics |
| title | Monthly Electric Energy Consumption Forecasting Using Multiwindow Moving Average and Hybrid Growth Models |
| title_full | Monthly Electric Energy Consumption Forecasting Using Multiwindow Moving Average and Hybrid Growth Models |
| title_fullStr | Monthly Electric Energy Consumption Forecasting Using Multiwindow Moving Average and Hybrid Growth Models |
| title_full_unstemmed | Monthly Electric Energy Consumption Forecasting Using Multiwindow Moving Average and Hybrid Growth Models |
| title_short | Monthly Electric Energy Consumption Forecasting Using Multiwindow Moving Average and Hybrid Growth Models |
| title_sort | monthly electric energy consumption forecasting using multiwindow moving average and hybrid growth models |
| url | http://dx.doi.org/10.1155/2014/243171 |
| work_keys_str_mv | AT mingmeng monthlyelectricenergyconsumptionforecastingusingmultiwindowmovingaverageandhybridgrowthmodels AT weishang monthlyelectricenergyconsumptionforecastingusingmultiwindowmovingaverageandhybridgrowthmodels AT dongxiaoniu monthlyelectricenergyconsumptionforecastingusingmultiwindowmovingaverageandhybridgrowthmodels |