scholarly journals Predicting Primary Energy Consumption Using Hybrid ARIMA and GA-SVR Based on EEMD Decomposition

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1722
Author(s):  
Yu-Sheng Kao ◽  
Kazumitsu Nawata ◽  
Chi-Yo Huang
Keyword(s):  
Energy Consumption ◽  
Empirical Study ◽  
Moving Average ◽  
Primary Energy ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Primary Energy Consumption ◽  
Mode Decomposition ◽  
Study Results ◽  
Hybrid Forecasting

Forecasting energy consumption is not easy because of the nonlinear nature of the time series for energy consumptions, which cannot be accurately predicted by traditional forecasting methods. Therefore, a novel hybrid forecasting framework based on the ensemble empirical mode decomposition (EEMD) approach and a combination of individual forecasting models is proposed. The hybrid models include the autoregressive integrated moving average (ARIMA), the support vector regression (SVR), and the genetic algorithm (GA). The integrated framework, the so-called EEMD-ARIMA-GA-SVR, will be used to predict the primary energy consumption of an economy. An empirical study case based on the Taiwanese consumption of energy will be used to verify the feasibility of the proposed forecast framework. According to the empirical study results, the proposed hybrid framework is feasible. Compared with prediction results derived from other forecasting mechanisms, the proposed framework demonstrates better precisions, but such a hybrid system can also be seen as a basis for energy management and policy definition.

Effectiveness of the GM(1,1) model on linear growth sequence and its application in global primary energy consumption prediction

Kybernetes ◽  
2016 ◽  
Vol 45 (9) ◽  
pp. 1472-1485 ◽  
Author(s):  
Chaoqing Yuan ◽  
Ding Chen
Keyword(s):  
Energy Consumption ◽  
Linear Growth ◽  
Moving Average ◽  
Arima Model ◽  
Primary Energy ◽  
Small Data ◽  
Primary Energy Consumption ◽  
Content Type ◽  
Energy Consumption Prediction

Purpose The purpose of this paper is to investigate the effectiveness of GM(1,1) model on linear growth sequences (LGS) by random experiments and global primary energy consumption is predicted as by the GM(1,1) and the autoregressive integrated moving average (ARIMA) model, which is used as a reference. Design/methodology/approach LGS generated randomly are used for GM(1,1) modeling. The results of the massive repeated random experiments are analyzed to test the effectiveness of the GM(1,1) model and global primary energy consumption is predicted using the GM(1,1) model and the ARIMA model. Findings The use of the GM(1,1) model is effective when used for a LGS and the model is proven to be reliable by the experiments. Global primary energy consumption is predicted with the GM(1,1) model and the ARIMA model as a case study, and the results show that GM(1,1) is quite good. Global primary energy consumption will increase by 1.03 percent in 2016. Originality/value The contribution of this paper includes the following: first, the applicability of the GM (1,1) model is further discussed with random experiments and it is feasible for a LGS; second, random experiments provide good proof that four data are enough for GM(1,1) modeling, and GM(1,1) model is reliable; third, prediction by using GM(1,1) model with small data is even better than time-series analysis in the case study.

Saving Primary Energy Consumption Through Exergy Analysis of Combine Distillation and Power Plant

2012 ◽  
Vol 9 (2) ◽  
pp. 65
Author(s):  
Alhassan Salami Tijani ◽  
Nazri Mohammed ◽  
Werner Witt
Keyword(s):  
Energy Consumption ◽  
Power Plant ◽  
Heat Pump ◽  
Heat Recovery ◽  
Energy Savings ◽  
Primary Energy ◽  
Heat Pumps ◽  
Saturated Steam ◽  
Distillation Unit ◽  
Primary Energy Consumption

Industrial heat pumps are heat-recovery systems that allow the temperature ofwaste-heat stream to be increased to a higher, more efficient temperature. Consequently, heat pumps can improve energy efficiency in industrial processes as well as energy savings when conventional passive-heat recovery is not possible. In this paper, possible ways of saving energy in the chemical industry are considered, the objective is to reduce the primary energy (such as coal) consumption of power plant. Particularly the thermodynamic analyses ofintegrating backpressure turbine ofa power plant with distillation units have been considered. Some practical examples such as conventional distillation unit and heat pump are used as a means of reducing primary energy consumption with tangible indications of energy savings. The heat pump distillation is operated via electrical power from the power plant. The exergy efficiency ofthe primary fuel is calculated for different operating range ofthe heat pump distillation. This is then compared with a conventional distillation unit that depends on saturated steam from a power plant as the source of energy. The results obtained show that heat pump distillation is an economic way to save energy if the temperaturedifference between the overhead and the bottom is small. Based on the result, the energy saved by the application of a heat pump distillation is improved compared to conventional distillation unit.

Hybrid approach combining EMD, ARIMA and monte carlo for multi-step ahead medical tourism forecasting

2021 ◽  
pp. 1-17
Author(s):  
Nuzhat Fatema ◽  
H Malik ◽  
Mutia Sobihah Binti Abd Halim
Keyword(s):  
Monte Carlo ◽  
Hybrid Model ◽  
Medical Tourism ◽  
Moving Average ◽  
Hybrid Approach ◽  
Mode Decomposition ◽  
Hybrid Forecasting ◽  
Input Variables ◽  
Intelligent Approach

This paper proposed a hybrid intelligent approach based on empirical mode decomposition (EMD), autoregressive integrated moving average (ARIMA) and Monte Carlo simulation (MCS) methods for multi-step ahead medical tourism (MT) forecasting using explanatory input variables based on two decade real-time recorded database. In the proposed hybrid model, these variables are 1st extracted then medical tourism is forecasted to perform the long term as well as the short term goal and planning in the nation. The multi-step ahead medical tourism is forecasted recursively, by utilizing the 1st forecasted value as the input variable to generate the next forecasting value and this procedure is continued till third step ahead forecasted value. The proposed approach firstly tested and validated by using international tourism arrival (ITA) dataset then proposed approach is implemented for forecasting of medical tourism arrival in nation. In order to validate the performance and accuracy of the proposed hybrid model, a comparative analysis is performed by using Monte Carlo method and the results are compared. Obtained results shows that the proposed hybrid forecasting approach for medical tourism has outperformance characteristics.

scholarly journals Decarbonization of Residential Building Energy Supply: Impact of Cogeneration System Performance on Energy, Environment, and Economics

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2538
Author(s):  
Praveen K. Cheekatamarla
Keyword(s):  
Carbon Dioxide ◽  
Power Generation ◽  
Energy Consumption ◽  
Carbon Footprint ◽  
Residential Buildings ◽  
Building Energy ◽  
Primary Energy ◽  
Primary Energy Consumption ◽  
Cogeneration System ◽  
The Impact

Electrical and thermal loads of residential buildings present a unique opportunity for onsite power generation, and concomitant thermal energy generation, storage, and utilization, to decrease primary energy consumption and carbon dioxide intensity. This approach also improves resiliency and ability to address peak load burden effectively. Demand response programs and grid-interactive buildings are also essential to meet the energy needs of the 21st century while addressing climate impact. Given the significance of the scale of building energy consumption, this study investigates how cogeneration systems influence the primary energy consumption and carbon footprint in residential buildings. The impact of onsite power generation capacity, its electrical and thermal efficiency, and its cost, on total primary energy consumption, equivalent carbon dioxide emissions, operating expenditure, and, most importantly, thermal and electrical energy balance, is presented. The conditions at which a cogeneration approach loses its advantage as an energy efficient residential resource are identified as a function of electrical grid’s carbon footprint and primary energy efficiency. Compared to a heat pump heating system with a coefficient of performance (COP) of three, a 0.5 kW cogeneration system with 40% electrical efficiency is shown to lose its environmental benefit if the electrical grid’s carbon dioxide intensity falls below 0.4 kg CO2 per kWh electricity.

scholarly journals Assessment Methodology for Efficiency, CO2-Emissions and Primary Energy Consumption for Refrigeration Technologies in the Industry

2018 ◽  
Vol 882 ◽  
pp. 215-220
Author(s):  
Matthias Koppmann ◽  
Raphael Lechner ◽  
Tom Goßner ◽  
Markus Brautsch
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Air Conditioning ◽  
Primary Energy ◽  
Primary Energy Consumption ◽  
Assessment Methodology ◽  
Alternative Technologies ◽  
Overall Efficiency ◽  
Chp System ◽  
The Individual

Process cooling and air conditioning are becoming increasingly important in the industry. Refrigeration is still mostly accomplished with compression chillers, although alternative technologies are available on the market that can be more efficient for specific applications. Within the scope of the project “EffiCool” a technology toolbox is currently being developed, which is intended to assist industrials users in selecting energy efficient and eco-friendly cooling solutions. In order to assess different refrigeration options a consistent methodology was developed. The refrigeration technologies are assessed regarding their efficiency, CO2-emissions and primary energy consumption. For CCHP systems an exergetic allocation method was implemented. Two scenarios with A) a compression chiller and B) an absorption chiller coupled to a natural gas CHP system were calculated exemplarily, showing a greater overall efficiency for the CCHP system, although the individual COP of the chiller is considerably lower.

Performance analysis of a combined cooling, heating, and power system driven by a waste biomass fired Stirling engine

2010 ◽  
Vol 225 (2) ◽  
pp. 420-428 ◽  
Author(s):  
J Harrod ◽  
P J Mago
Keyword(s):  
Energy Consumption ◽  
Natural Gas ◽  
Wood Chip ◽  
Primary Energy ◽  
Stirling Engine ◽  
Primary Energy Consumption ◽  
Operational Cost ◽  
Excess Production ◽  
Prime Movers ◽  
Combined Cooling

Due to the soaring costs and demand of energy in recent years, combined cooling, heating, and power (CCHP) systems have arisen as an alternative to conventional power generation based on their potential to provide reductions in cost, primary energy consumption, and emissions. However, the application of these systems is commonly limited to internal combustion engine prime movers that use natural gas as the primary fuel source. Investigation of more efficient prime movers and renewable fuel applications is an integral part of improving CCHP technology. Therefore, the objective of this study is to analyse the performance of a CCHP system driven by a biomass fired Stirling engine. The study is carried out by considering an hour-by-hour CCHP simulation for a small office building located in Atlanta, Georgia. The hourly thermal and electrical demands for the building were obtained using the EnergyPlus software. Results for burning waste wood chip biomass are compared to results obtained burning natural gas to illustrate the effects of fuel choice and prime mover power output on the overall CCHP system performance. Based on the specified utility rates and including excess production buyback, the results suggest that fuel prices of less than $23/MWh must be maintained for savings in cost compared to the conventional case. In addition, the performance of the CCHP system using the Stirling engine is compared with the conventional system performance. This comparison is based on operational cost and primary energy consumption. When electricity can be sold back to the grid, results indicate that a wood chip fired system yields a potential cost savings of up to 50 per cent and a 20 per cent increase in primary energy consumption as compared with the conventional system. On the other hand, a natural gas fired system is shown to be ineffective for cost and primary energy consumption savings with increases of up to 85 per cent and 24 per cent compared to the conventional case, respectively. The variations in the operational cost and primary energy consumption are also shown to be sensitive to the electricity excess production and buyback rate.

scholarly journals A New Hybrid Approach for Wind Speed Forecasting Applying Support Vector Machine with Ensemble Empirical Mode Decomposition and Cuckoo Search Algorithm

2018 ◽  
Vol 8 (10) ◽  
pp. 1754 ◽  
Author(s):  
Tongxiang Liu ◽  
Shenzhong Liu ◽  
Jiani Heng ◽  
Yuyang Gao
Keyword(s):  
Wind Speed ◽  
Search Algorithm ◽  
Wind Farms ◽  
Cuckoo Search ◽  
Ensemble Empirical Mode Decomposition ◽  
Cuckoo Search Algorithm ◽  
Support Vector ◽  
Wind Speed Forecasting ◽  
Mode Decomposition ◽  
Proposed Model

Wind speed forecasting plays a crucial role in improving the efficiency of wind farms, and increases the competitive advantage of wind power in the global electricity market. Many forecasting models have been proposed, aiming to enhance the forecast performance. However, some traditional models used in our experiment have the drawback of ignoring the importance of data preprocessing and the necessity of parameter optimization, which often results in poor forecasting performance. Therefore, in order to achieve a more satisfying performance in forecasting wind speed data, a new short-term wind speed forecasting method which consists of Ensemble Empirical Mode Decomposition (EEMD) for data preprocessing, and the Support Vector Machine (SVM)—whose key parameters are optimized by the Cuckoo Search Algorithm (CSO)—is developed in this paper. This method avoids the shortcomings of some traditional models and effectively enhances the forecasting ability. To test the prediction ability of the proposed model, 10 min wind speed data from wind farms in Shandong Province, China, are used for conducting experiments. The experimental results indicate that the proposed model cannot only improve the forecasting accuracy, but can also be an effective tool in assisting the management of wind power plants.

Sign in / Sign up

Export Citation Format

Share Document