Results 1 - 10 of 391
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[en] This study addresses the problem of modeling the electricity demand loads in Greece. The provided actual load data is deseasonilized and an AutoRegressive Moving Average (ARMA) model is fitted on the data off-line, using the Akaike Corrected Information Criterion (AICC). The developed model fits the data in a successful manner. Difficulties occur when the provided data includes noise or errors and also when an on-line/adaptive modeling is required. In both cases and under the assumption that the provided data can be represented by an ARMA model, simultaneous order and parameter estimation of ARMA models under the presence of noise are performed. The produced results indicate that the proposed method, which is based on the multi-model partitioning theory, tackles successfully the studied problem. For validation purposes the produced results are compared with three other established order selection criteria, namely AICC, Akaike's Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). The developed model could be useful in the studies that concern electricity consumption and electricity prices forecasts
[en] To improve energy efficiency, extensive studies have focused on the cutting parameters optimization in the machining process. Actually, non-cutting activities (NCA) occur frequently during machining and this is a promising way to save energy through optimizing NCA without changing the cutting parameters. However, it is difficult for the existing methods to accurately determine and reduce the energy wastes (EW) in NCA. To fill this gap, a novel Therblig-embedded Value Stream Mapping (TVSM) method is proposed to improve the energy transparency and clearly show and reduce the EW in NCA. The Future-State-Map (FSM) of TVSM can be built by minimizing non-cutting activities and Therbligs. By implementing the FSM, time and energy efficiencies can be improved without decreasing the machining quality, which is consistent with the goal of lean energy machining. The method is validated by a machining case study, the results show that the total energy is reduced by 7.65%, and the time efficiency of the value-added activities is improved by 8.12%, and the energy efficiency of value-added activities and Therbligs are raised by 4.95% and 1.58%, respectively. This approach can be applied to reduce the EW of NCA, to support designers to design high energy efficiency machining processes during process planning. - Highlights: • Therblig-embedded value stream mapping is proposed for energy management of machining process. • Current state map and future state map of TVSM are established for energy saving. • Machining more material with less energy without lowering the machining quality. • Total energy demand is reduced by 7.65% through implementing the proposed method. • Energy efficiency of value added activity/Therblig are improved by 4.95% and 1.58%, respectively.
[en] Energy benchmarking has been recognized as an effective analytical methodology and management tool to improve energy efficiency and performance. Many approaches to energy benchmarking have been applied in various fields. Machining systems, which are widely distributed and consume large amounts of energy with low efficiency, possess considerable potential for reductions in energy consumption. However, current research regarding the use of energy benchmarking for machining systems is insufficient due to the complexity and variety of energy consumption processes used in these systems. This paper proposes the use of energy benchmarking to strengthen the evaluation of energy demand and achieve efficiency improvements for machining systems. First, it analyses drivers for energy benchmarking and their characteristics. Next, an energy benchmarking framework for machining systems is presented. Then the concepts of the static, dynamic, single-objective, multi-objective, product-based, and process-based energy benchmarking are discussed from three different perspectives: the motion, object and application level. This lays a theoretical foundation for further energy benchmarking research. Finally, methods for developing energy benchmarking are also addressed including the prediction method, statistical analysis and expert decision. The application of these methods to a real machining plant allows an analysis of the practicability of potentially saving energy through benchmarking. - Highlights: • Energy benchmarking for assessing the energy demand and energy was proposed. • Drivers of energy benchmarking and their characteristics were analysed. • The energy benchmarking framework of machining systems was conducted. • Some concepts about energy benchmarking (e.g. static energy benchmarking) were presented. • Methods for developing the energy benchmarking were addressed.
[en] In this paper, we introduce the combination of an energy-process model with the geometric distributed lag demand, called the energy-GDL process model, as well as its solution technique. In an energy-GDL process model, the demand is represented by a function of the prices not only in the current time period but also in previous time periods, based on the geometric, distributed lag structure. The supply is a cost-minimizing linear process submodel. The software WATEMS-GDL (the GDL version of the Waterloo Energy Modelling System) is implemented for solving such a model. An example illustrating the procedure of modelling and equilibrium-seeking is given. (Author)
[en] Decomposition analysis can provide a useful efficiency metric for the industrial sector, it does so by separating the influence of changes in energy intensity from structural and activity changes. The activity refactorisation (AR) approach to decomposition analysis offers a potentially improved methodology, with a better correlation to real efficiency improvements. This is achieved through combining monetary and physical output data. Here the AR approach is compared to other methodologies for the United Kingdom industrial sector over the period 1997–2012. Even with limited availability of physical output data the AR approach was found to provide significantly different results to those obtained using only monetary output measures. When monetary output was the sole measure of activity, intensity (efficiency) improvements were overestimated. It is recommended that physical output data is supplied alongside energy demand and monetary output data in national accounts to better track efficiency improvements and allow such improved metrics to feed into decarbonisation policy discussions. - Highlights: • Decomposition analysis is applied to provide an industrial energy efficiency metric. • The activity refactorisation approach to decomposition analysis is explored. • Physical and monetary measures of output are combined in this approach. • Data availability can often be a barrier to this form of analysis. • Recommendations are made for improving national datasets to track efficiency.
[en] It is an extremely difficult task to project Asia-Pacific energy trends to 2010 with any degree of certainty, partly because the region is a complex mix of developed, developing, and newly industrialized economies and has the world's fastest economic growth. In addition, because of the region's large and ever increasing dependence on imported crude oil, numerous external factors must be integrated into the analysis, including world oil prices and supply in particular. Similarly, the issue of coal and gas substitution is a vital but uncertain factor, because of the great difficulties involved in projecting energy developments in China and the countries of the former Soviet Union or the impact of such developments on the region. The countries of the former Soviet Union are not included in the regional data presented in this article but are discussed separately in article 4. (author)
[en] This paper describes the development of the Energy and Climate Policy and Scenario Evaluation (ECLIPSE) model-a flexible integrated assessment tool for energy and climate change policy and scenario assessment. This tool builds on earlier efforts to link top-down and bottom-up models, and combines a macroeconomic energy demand model and a consumer-budget transport demand model with the technology-rich bottom-up energy and transport system model Energy Research and Investment Strategy (ERIS), and solves the models iteratively. Compared to previous efforts, ECLIPSE includes many new features, such as a more disaggregated production function, improved calibration and parameterization and separate modeling of passenger transport demand. The separate modeling of transportation makes ECLIPSE particularly well-suited to analyzing interactions between the transport sector and the broader energy market and economy. This paper presents results illustrating some features of the integrated model, compares technology deployment results with ECLIPSE and the bottom-up ERIS model, and briefly describes illustrative baseline and greenhouse gas mitigation scenarios to highlight some of the features of the framework outlined in this paper. A number of modeling and policy insights arising from this scenario analysis are discussed
[en] Cooling system consumes more than 35% of total electricity in most data centers. The provided cooling normally exceeds the actual demand of IT equipment in order to assure the safe operation, resulting in a low energy efficiency. In this paper, a novel method based on demand response was proposed to precisely control the cooling supply, and the energy saving potential was assessed systematically. Compared to the reference case, in which the cooling demand is determined by assuming all of servers are in the running status, when the cooling demand was determined based on the measured dynamic IT load at room level, row level, rack level and server level, it can be reduced by 7.9%, 14.2%, 15.6% and 17.9% respectively for the random selected 48 h. In addition, IT load shifting also has a big potential to save energy, as it can make the cooling system working at a higher energy efficiency, which varies with loads. Two cases were studied: even distribution of IT load and optimized IT load shifting. Compared to the best case that determines the cooling demand according to the IT load at server level, they can further reduce the electricity consumption of cooling systems by 0.9%, and 1.2%. - Highlights: • Energy saving potentials of determining cooling demand based on IT load are studied. • Determining cooling based IT load at server level has the lowest energy consumption. • IT load shifting is able to further reduce electricity consumption of cooling units.
[en] We examine the experiences that states and utilities are having with the NLRA approach. Contrary to concerns raised by some industry analysts, our results indicate the NLRA is a feasible approach to the lost-revenue disincentive. Seven of the 10 states we studied report no substantial problems with their approach. We observe several conditions linked to effective NLRA implementation and, for those states reporting problems, conditions linked to implementation difficulties. Finally, observed changes in utility-investment behavior occur after implementation of DSM rate reforms, which include deployment of NLRA mechanisms. We find that utilities in states with lost revenue recovery invest more than twice as much in DSM as do utilities in other states. (Author)
[en] The importance of energy demand estimation stems from energy planning, formulating strategies and recommending energy policies. Most often, energy demand is mathematically formulated by socio-economic indicators. The challenging problem is to determine the optimal or near optimal weighting factors. Inspired by social behavior of bird flocking or fish schooling, PSO (particle swarm optimization) is a population-based search technique which has attracted significant attention to tackle the complexity of difficult optimization problems. This paper studies the performance of different PSO variants for estimating Iran's electricity demand. Seven PSO variants namely, original PSO, PSO-w (PSO with weighting factor), PSO-cf (PSO with constriction factor), PSO-rf (PSO with repulsion factor), PSO-vc (PSO with velocity control), CLPSO (comprehensive learning PSO) and a MPSO (modified PSO), are used to find the unknown weighting factors based on the data from 1982 to 2003. The validation process is then conducted by testing the optimized models by using the data from 2004 to 2009. It is seen that PSO-vc produces more promising results than the other variants, HS (harmony search) and ABSO (artificial bee swarm optimization) algorithms in terms of MAPE (mean absolute percentage error). This value is obtained 2.47 and 2.50 for the exponential and quadratic models, respectively. - Highlights: • Electricity demand estimation is modelled using socio-economic indicators. • Different PSO variants are investigated in terms of accuracy. • Exponential model can estimate the Iran's electricity demand with high accuracy. • PSO with velocity control produces more accurate result than the others