scholarly journals A Novel Adaptive Intelligent Ensemble Model for Forecasting Primary Energy Demand

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1347 ◽  
Author(s):  
Wenting Zhao ◽  
Juanjuan Zhao ◽  
Xilong Yao ◽  
Zhixin Jin ◽  
Pan Wang
Keyword(s):  
Energy Demand ◽  
Pso Algorithm ◽  
Primary Energy ◽  
Small Samples ◽  
Support Vector ◽  
Energy Structure ◽  
Ensemble Model ◽  
Primary Energy Demand ◽  
Energy Demands ◽  
Development Plans

Effectively forecasting energy demand and energy structure helps energy planning departments formulate energy development plans and react to the opportunities and challenges in changing energy demands. In view of the fact that the rolling grey model (RGM) can weaken the randomness of small samples and better present their characteristics, as well as support vector regression (SVR) having good generalization, we propose an ensemble model based on RGM and SVR. Then, the inertia weight of particle swarm optimization (PSO) is adjusted to improve the global search ability of PSO, and the improved PSO algorithm (APSO) is used to assign the adaptive weight to the ensemble model. Finally, in order to solve the problem of accurately predicting the time-series of primary energy consumption, an adaptive inertial weight ensemble model (APSO-RGM-SVR) based on RGM and SVR is constructed. The proposed model can show higher prediction accuracy and better generalization in theory. Experimental results also revealed outperformance of APSO-RGM-SVR compared to single models and unoptimized ensemble models by about 85% and 32%, respectively. In addition, this paper used this new model to forecast China’s primary energy demand and energy structure.

scholarly journals 2030 Energy Pathways in Côte d’Ivoire: “A Business as Usual” Analysis

2019 ◽  
Vol 7 (6) ◽  
Author(s):  
Yessoh Gaudens Thecle Edjoukou ◽  
Bangzhu Zhu ◽  
Minxing Jiang ◽  
Akadje Jean Roland Edjoukou
Keyword(s):  
Energy Demand ◽  
Moving Average ◽  
Resource Planning ◽  
Primary Energy ◽  
Steady Increase ◽  
Energy Structure ◽  
Annual Growth Rate ◽  
Autoregressive Integrated Moving Average ◽  
Primary Energy Demand ◽  
Business As Usual

Forecasting future energy demand values is of paramount importance for proper resource planning. This paper examines energy outlook for the coming decade in Côte d’Ivoire presented as a business as usual scenario. We, therefore, build a forecasting model using the Autoregressive Integrated Moving Average (ARIMA) to estimate primary energy demand and energy demand by fuels. The results indicate that energy demand will increase steadily within the forecasted period (2017-2030). However, the annual growth rate of each fuel,, including the primary energy demand item, will first rise from the year 1990 to the year 2016 and then decrease within the forecasted period except hydropower that will experience a steady increase from 1990 to 2030. Furthermore, it is noticed that the energy structure of the country will still be biofuels (fuelwood and charcoal) intensive with a significant presence of conventional sources of energy. Based on these findings, we propose some policy recommendations.

scholarly journals Impact Of Building Integrated Photovoltaics (BIPV) In Architectural Science Building (ARC) Ryerson University

2021 ◽  
Author(s):  
Amit Gurung
Keyword(s):  
Energy Demand ◽  
Primary Energy ◽  
Solar Heat ◽  
Building Integrated Photovoltaics ◽  
Primary Energy Demand ◽  
Energy Demands ◽  
Lighting Energy ◽  
Science Building ◽  
Window Blinds ◽  
Building Operation

Solar heat gains, heating, cooling and lighting energy demands are the primary energy associated with building operation. Glare and solar heat gains are the common issues in the buildings with high window to wall ratio. Window blinds are commonly used to control the glare which blocks the natural lights as well. Scientifically designed external shading devise also helps to control glare which are merely used in the tall modern glass buildings. So renewable technologies like Building Integrated Photovoltaics (BIPV) can be one of the strategies to address the primary energy demand of the building, glare control as well as on site electricity generation. The study includes the performance of BIPV application in the faculty office area of third floor of ARC building at Ryerson University. It shows that the BIPV can be effective by addressing the lighting, cooling demand effectively.

scholarly journals Towards an Automated, Fast and Interpretable Estimation Model of Heating Energy Demand: A Data-Driven Approach Exploiting Building Energy Certificates

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1273 ◽  
Author(s):  
Antonio Attanasio ◽  
Marco Piscitelli ◽  
Silvia Chiusano ◽  
Alfonso Capozzoli ◽  
Tania Cerquitelli
Keyword(s):  
Decision Tree ◽  
Energy Demand ◽  
Building Energy ◽  
Energy Performance ◽  
Primary Energy ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Support Vector ◽  
Estimation Model ◽  
Primary Energy Demand

Energy performance certification is an important tool for the assessment and improvement of energy efficiency in buildings. In this context, estimating building energy demand also in a quick and reliable way, for different combinations of building features, is a key issue for architects and engineers who wish, for example, to benchmark the performance of a stock of buildings or optimise a refurbishment strategy. This paper proposes a methodology for (i) the automatic estimation of the building Primary Energy Demand for space heating ( P E D h ) and (ii) the characterization of the relationship between the P E D h value and the main building features reported by Energy Performance Certificates (EPCs). The proposed methodology relies on a two-layer approach and was developed on a database of almost 90,000 EPCs of flats in the Piedmont region of Italy. First, the classification layer estimates the segment of energy demand for a flat. Then, the regression layer estimates the P E D h value for the same flat. A different regression model is built for each segment of energy demand. Four different machine learning algorithms (Decision Tree, Support Vector Machine, Random Forest, Artificial Neural Network) are used and compared in both layers. Compared to the current state-of-the-art, this paper brings a contribution in the use of data mining techniques for the asset rating of building performance, introducing a novel approach based on the use of independent data-driven models. Such configuration makes the methodology flexible and adaptable to different EPCs datasets. Experimental results demonstrate that the proposed methodology can estimate the energy demand with reasonable errors, using a small set of building features. Moreover, the use of Decision Tree algorithm enables a concise interpretation of the quantitative rules used for the estimation of the energy demand. The methodology can be useful during both designing and refurbishment of buildings, to quickly estimate the expected building energy demand and set credible targets for improving performance.

The Feasibility Analysis and Pathways Study of China’s 2020 Non-Fossil Energy Target

2013 ◽  
Vol 869-870 ◽  
pp. 559-563
Author(s):  
Yan Xu ◽  
Jia Hai Yuan
Keyword(s):  
Economic Growth ◽  
Energy Demand ◽  
Clean Energy ◽  
Primary Energy ◽  
Energy Planning ◽  
Energy Structure ◽  
Chinese Government ◽  
Fossil Energy ◽  
Primary Energy Demand ◽  
Energy Share

In 2009, Chinese government proposed the 15% target of non-fossil energy share at 2020, and obtained the general concern of the international community. The paper considers the constraints of economic growth, decrease rate of GDP CO2 intensity and primary energy structure to discuss China’s CO2 emissions and primary energy demand in sub-scenarios. Then through the analysis of consistency with the overall economic growth and energy planning and the international society’s expectation on China’s GHG abatement duty to demonstrate the feasibility of the non-fossil energy target. The results show that the 17% of non-fossil energy can meet the various constraints. Finally pathways to realize clean energy development into 2020 are outlined.

scholarly journals Impact Of Building Integrated Photovoltaics (BIPV) In Architectural Science Building (ARC) Ryerson University

2021 ◽  
Author(s):  
Amit Gurung
Keyword(s):  
Energy Demand ◽  
Primary Energy ◽  
Solar Heat ◽  
Building Integrated Photovoltaics ◽  
Primary Energy Demand ◽  
Energy Demands ◽  
Lighting Energy ◽  
Science Building ◽  
Window Blinds ◽  
Building Operation

Solar heat gains, heating, cooling and lighting energy demands are the primary energy associated with building operation. Glare and solar heat gains are the common issues in the buildings with high window to wall ratio. Window blinds are commonly used to control the glare which blocks the natural lights as well. Scientifically designed external shading devise also helps to control glare which are merely used in the tall modern glass buildings. So renewable technologies like Building Integrated Photovoltaics (BIPV) can be one of the strategies to address the primary energy demand of the building, glare control as well as on site electricity generation. The study includes the performance of BIPV application in the faculty office area of third floor of ARC building at Ryerson University. It shows that the BIPV can be effective by addressing the lighting, cooling demand effectively.

A PSO–GA optimal model to estimate primary energy demand of China

Energy Policy ◽  
2012 ◽  
Vol 42 ◽  
pp. 329-340 ◽  
Author(s):  
Shiwei Yu ◽  
Yi-Ming Wei ◽  
Ke Wang
Keyword(s):  
Energy Demand ◽  
Primary Energy ◽  
Optimal Model ◽  
Primary Energy Demand

An environmental impact analysis method of machine-tool cutting units based on LCA

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Junli Shi ◽  
Junyu Hu ◽  
Mingyang Ma ◽  
Huaizhi Wang
Keyword(s):  
Environmental Impact ◽  
Machine Tool ◽  
Energy Demand ◽  
Impact Analysis ◽  
Primary Energy ◽  
Analysis Method ◽  
Environmental Impact Analysis ◽  
Content Type ◽  
Primary Energy Demand ◽  
Tool Cutting

Purpose The purpose of this paper is to present a method for the environmental impact analysis of machine-tool cutting, which enables the detailed analysis of inventory data on resource consumption and waste emissions, as well as the quantitative evaluation of environmental impact. Design/methodology/approach The proposed environmental impact analysis method is based on the life cycle assessment (LCA) methodology. In this method, the system boundary of the cutting unit is first defined, and inventory data on energy and material consumptions are analyzed. Subsequently, through classification, five important environmental impact categories are proposed, namely, primary energy demand, global warming potential, acidification potential, eutrophication potential and photochemical ozone creation potential. Finally, the environmental impact results are obtained through characterization and normalization. Findings This method is applied on a case study involving a machine-tool turning unit. Results show that primary energy demand and global warming potential exert the serious environmental impact in the turning unit. Suggestions for improving the environmental performance of the machine-tool turning are proposed. Originality/value The environmental impact analysis method is applicable to different machine tools and cutting-unit processes. Moreover, it can guide and support the development of green manufacturing by machinery manufacturers.

Optimization of the energy supply in the plastics industry to reduce the primary energy demand

2018 ◽  
Vol 192 ◽  
pp. 790-800 ◽  
Author(s):  
Heiko Dunkelberg ◽  
Johannes Wagner ◽  
Conrad Hannen ◽  
B. Alexander Schlüter ◽  
Long Phan ◽  
...  
Keyword(s):  
Energy Supply ◽  
Energy Demand ◽  
Primary Energy ◽  
Primary Energy Demand ◽  
Plastics Industry

scholarly journals Myth v. Fact

2011 ◽  
Vol 133 (01) ◽  
pp. 24-29 ◽  
Author(s):  
John Reilly ◽  
Allison Crimmins
Keyword(s):  
Energy Demand ◽  
Alternative Energy ◽  
Nuclear Energy ◽  
Fossil Fuels ◽  
Fossil Fuel ◽  
Primary Energy ◽  
Electricity Production ◽  
Energy Prices ◽  
Primary Energy Demand ◽  
Business As Usual

This article predicts future global energy demand under a business-as-usual scenario. According to the MIT projections, conventional technology supported by fossil fuels will continue to dominate under a business-as-usual scenario. In fact, in the absence of climate policies that would impact energy prices, fossil fuels will supply nearly 80% of global primary energy demand in 2100. Alternative energy technologies will expand rapidly. Non-fossil fuel use will grow from 13% to 20% by 2100, with renewable electricity production expanding nearly tenfold and nuclear energy increasing by a factor of 8.5. However, those sources currently provide such a small share of the world's energy that even rapid growth is not enough to significantly displace fossil fuels. In spite of the growth in renewables, the projections indicate that coal will remain among the least expensive fuel sources. Non-fossil fuel alternatives, such as renewable energy and nuclear energy, will be between 40% and 80% more expensive than coal.

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