Estimation Of Energy Demand Research Articles

How sensitive to time period sampling is the asymmetric price response specification in energy demand modelling?

The purpose of this paper is to investigate the criticism that energy demand estimates based on a specific price decomposition are sensitive to the chosen time period used for the estimation. To analyse this in a systematic way, different time series sample periods are constructed from annual data for 17 OECD countries covering the overall period 1960 to 2008. The specific price decomposition under consideration, often used to estimate asymmetric price response models of energy demand, separates the impact of prices above the previous maximum, of a price recovery below the previous maximum and of a price cut. Therefore, the analysis does not just involve using different time periods; instead, for each time period investigated, a new dataset is constructed and for each dataset, the price variable is decomposed in this way. An energy demand relationship allowing for asymmetric price responses is therefore estimated for each different sample period and the results suggest that recalculation of the decomposed price variables for each different period does affect the stability of the estimated energy demand responses. In contrast, a similarly estimated energy demand relationship with symmetric price responses for each different sample period is found to have less instability.

A POST-KEYNESIAN AGE MODEL TO FORECAST ENERGY DEMAND IN SPAIN

This paper develops an extended input–output model for the estimation of energy demand and related issues. It is built on the last Spanish Symmetric Input–Output Table (IOT, 2005). It has been tested for the period 2005–2008 and used for forecasting energy demand for the years 2009–2012 under different economic scenarios. The model shares some traits of the computable and applied general equilibrium models where quantity and price systems are interwoven. The differences lie in the theories explaining output and prices. Our quantity system is based on Keynes’ principle of effective demand (broad energy multipliers are derived). The price system is based on the classical (Sraffian) theory of prices of production, akin to post-Keynesian full-cost prices. The general price system can be manipulated to account for the specificities of energy prices. Historical trends of energy coefficients are computed by extrapolation of past IOTs and calibration.

Development and implementation of a simplified residential energy asset rating model

Recent European legislation (Energy Efficiency Directive) has allocated some responsibility for residential end use energy efficiency to energy supply companies. In order to overcome data and modelling limitations associated with statistical and engineering modelling approaches to energy efficiency and renewable energy retrofit measures, energy suppliers and policy-makers often use simplified methods with limited data requirements to assess dwellings. One approach employed is an asset rating method (ARM); a standardised approach to residential energy demand estimation which is outlined in ISO EN 13790 (Energy Performance of Buildings Directive). Although it is a simplified method which industry is well-equipped to deliver, it is time-consuming to apply ARMs to the large domestic customer bases of energy suppliers. A small per-dwelling time saving will result in significant overall efficiencies for these users. This study examines the effect that reducing input data requirements of the ARM has on the accuracy of the methodology and comments on the trade-off between model simplification and accuracy. We find that it is possible to maintain a high degree of accuracy (∼95%) with 20 fewer variables than the baseline model. This is equivalent to almost 40% fewer variables than in the full model and represents a significant saving in effort

Promoting New Links Between Energy and Meteorology

T he growing body of knowledge and experience in weather and climate risk management in the energy industry has spurred a rapidly growing research interest at the nexus between weather, climate, and energy (Troccoli 2010; Troccoli et al. 2010; Ebinger and Vergara 2011; Marquis 2011). Although this increased attention has been stimulated by a renewed and fervent interest in renewable energy sources, weather and climate information is also critical to managing the energy supply from other energy sectors (e.g., offshore oil operations) as well as understanding and estimating energy demand. Until recently, such applied research was discussed in specialized sessions during conferences organized by industry-specific organizations related to wind energy (e.g., American Wind Energy Association), solar energy [e.g., Solar Power and Chemical Energy Systems (SolarPACES)], and meteorology (e.g., European Meteorological Society). To take advantage of the substantial overlap between these energy activities and their use of weather and climate information, the International Conference Energy and Meteorology (ICEM) 2011 (www.icem2011.org/) was convened in Queensland, Australia. The objective was to provide a dedicated forum where scientists, engineers, economists, policy makers, and other specialists and practitioners involved in research or implementation activities at the intersection between weather, climate, and energy could discuss recent research findings and emerging practices ranging from operational activities to longterm investment planning and to policy making. Such discussions were framed within the context of all energy sector uses, including climate change mitigation and weather/climate risk management practices. The four expected outcomes of this conference were as follows:

A recombination-based hybridization of particle swarm optimization and artificial bee colony algorithm for continuous optimization problems

This paper presents a hybridization of particle swarm optimization (PSO) and artificial bee colony (ABC) approaches, based on recombination procedure. The PSO and ABC are population-based iterative methods. While the PSO directly uses the global best solution of the population to determine new positions for the particles at the each iteration, agents (employed, onlooker and scout bees) of the ABC do not directly use this information but the global best solution in the ABC is stored at the each iteration. The global best solutions obtained by the PSO and ABC are used for recombination, and the solution obtained from this recombination is given to the populations of the PSO and ABC as the global best and neighbor food source for onlooker bees, respectively. Information flow between particle swarm and bee colony helps increase global and local search abilities of the hybrid approach which is referred to as Hybrid approach based on Particle swarm optimization and Artificial bee colony algorithm, HPA for short. In order to test the performance of the HPA algorithm, this study utilizes twelve basic numerical benchmark functions in addition to CEC2005 composite functions and an energy demand estimation problem. The experimental results obtained by the HPA are compared with those of the PSO and ABC. The performance of the HPA is also compared with that of other hybrid methods based on the PSO and ABC. The experimental results show that the HPA algorithm is an alternative and competitive optimizer for continuous optimization problems.

Trends in Cooling Degree Days for Building Energy Estimation in Latvia

Calculation of cooling degree days (CDDs) and hours (CDHs) is one of the simplest methods to describe the effect of outside air temperature on building cooling energy consumption. Calculations of CDD numbers and analysis of trends were performed using 10-year data of outdoor temperature, measured at the meteorological station in Riga (Latvia). Values of CDDs and CDHs for seven base temperatures (from 19.0°C to 26.0°C) are provided in a tabular form. Results showed an increase in CDDs by the year 2020. Findings of this study could be further used for energy management in Riga region. I. INTRODUCTION Nowadays the building sector in member states of the European Union accounts for about 40% of final energy consumption and about a third of greenhouse gas emissions, of which about two-thirds are ascribed to residential and one- third to commercial buildings (1). Even though most of energy in eastern European buildings is used for space heating, e.g., about 70% in Latvian households (2), special attention should also be directed towards space cooling, since the energy consumption it accounts for (mostly electrical energy) is growing rapidly. It is expected that energy demand for cooling will increase even more in the future due to global warming, higher demands for quality of life and comfort, an increased use of glazing in architecture, increased number of electric appliances etc. and generally due to economic growth (3). Heating and cooling energy consumption in buildings is mostly affected by outdoor temperature changes (4; 5), where spatial and temporal considerations also play an important role. Several methods are available for energy analysis and estimation of energy demands. These methods differ in complexity, in amount of ambient condition data that needs to be taken into account, time increment used for calculations, detail of description of the building geometry and its characteristics etc. (6). Degree day method is currently the simplest and well-established technique for energy analysis, providing adequate results for simple systems and applications. It is appropriate in cases when the use of building, efficiency of HVAC equipment, indoor temperature and internal gains are relatively constant. Degree day method allows for the comparison of building current and past energy performance, as well as the comparison of buildings in different climate zones. In (7) cooling degree day data for 171 countries was summarized, where Mali and Nigeria were ranked 1 st and 2 nd for having the highest number of cooling

Estimating the balance between pasture feed supply and demand of grazing livestock in a farmlet experiment

Data from the Cicerone farmlet study were used to quantify the balance between pasture feed supply and the demand from grazing livestock, in terms of metabolisable energy (ME), on three differently managed farmlets (each of 53 ha) on the Northern Tablelands of New South Wales, Australia. Farmlet A had a high level of pasture renovation and higher soil fertility than the other two farmlets and employed flexible grazing management over eight paddocks. Farmlet B was designed to represent management ‘typical’ of the region and had the same grazing management and number of paddocks as farmlet A but moderate levels of pasture renovation and soil fertility. The third farmlet (C) had the same level of inputs as farmlet B but practised intensive rotational grazing over 37 paddocks. Regular measurements of the feed supply, namely herbage mass and quality, pasture growth and supplement fed and of feed demand were assembled to provide monthly estimates of the balance between feed supply and animal demand of all classes of livestock run on the experiment over its duration of 6.5 years. The significantly greater stocking rate, liveweight and reproductive rate of sheep reached on the higher input system (farmlet A) meant higher levels of ME were required to satisfy the nutritional demands of these animals. As only limited measurements were taken of animal intake, it was assumed that the supply of ME was derived from pasture growth and supplement fed. Using key livestock management dates and measurements of liveweights, the changes in the energy requirements of each class of animal were calculated and aggregated to provide an estimate of overall livestock energy demand over time. Subtracting the energy demand from the estimated energy supply provided a partial net energy balance. Measurements of the rates of change of green herbage during grazing events were found to be highly dependent on stock density with farmlets A, B and C recording rates of change of up to –50, –30 and –200 green DM/ha.day, respectively. Over a series of generally drier-than-average years, the ME supplied in pasture growth and through supplementation was at times inadequate to meet the energy demands of the livestock, resulting in periods during winter when the partial energy balance became negative. Similar feed deficits were observed for all three farmlets, suggesting that they were over-stocked to a similar extent. In spite of the divergence in the stocking rate supported by each farmlet, the similarity of the ME balances between farmlets suggests that no farmlet was subjected to bias because of decisions relating to feed supply and demand. The analyses presented suggest there is considerable potential for practical paddock and grazing management to be improved if more timely and regular assessments can be made of changes in the feed energy supply using satellite images of normalised difference vegetation indices and feed energy demand using calculations of the ME required by grazing livestock.

An Energy and Water Resource Demand Estimation Model for Multi-Family Housing Complexes in Korea

This paper proposes and develops a residential energy and resource consumption estimation model in the context of multi-family residential housing in Korea using a multi-layer perceptron (MLP) neural network. Eight indicators are introduced which affect the energy and water resource usage characteristics of Korean residential complexes. The proposed model precisely estimated the electricity, gas energy and water consumption for each examined residential complex. In terms of validation, the results showed the highest level of agreement with actually collected datasets. The model shows promising prospects in providing necessary estimations, not only for optimally scaling and sizing energy- and water-related infrastructures, but also to promote reliable energy and resource savings through greenhouse gas (GHG) reduction planning in multi-family housing complexes. The model could also be of use in framing guidelines for the better planning of national or regional energy and resource policies and for forming a foundation of decision-making with definite references regarding the facility management of each apartment complex to enhance the energy and resource use efficiency at these locations.

Forecasting of daily natural gas consumption on regional basis in Turkey using various computational methods

It is widely accepted that natural gas is a clean energy source that can be used to meet energy demand for heating and industrial purpose among the fossil fuels and its usage remarkably increases in order to maintain a clean environment in many countries in the world. It is fact that this makes energy investment planning in a country or region highly important for suitable economic development as well as environmental aspect. Therefore, energy demand for various sectors should be estimated in the frame of short-term energy policy. For accurate estimation of short-term energy demand a limited number of computational methods are employed by using the 4 yearly measured natural gas consumption values. Among these methods, the ANN and time series are widely used for short-term estimation of natural gas consumption in Turkey's certain regions. In this study, multilayer perceptron the ANNs with time series approach is proposed to forecast short-term natural gas consumption. Meteorological data (moisture, atmospheric pressure, wind speed and ambient temperature) obtained from the regional gas distribution company and the local meteorology office in last 4 years to construct well-tuned algorithm. Although the number of data was small, the proposed algorithm works well to forecast the short-term natural gas consumption and produces encouraging and meaningful outcomes for future energy investment policy.

Desalination using membrane distillation: Flux enhancement by feed water deaeration on spiral-wound modules

Membrane distillation (MD) is considered a promising technology for desalination applications. In this paper, the main transport phenomena in membrane distillation are analyzed theoretically. One of the main limiting factors is the molecular diffusion resistance caused by stagnant air in the membrane pore volume. A great potential for flux enhancement has been identified to be the removal of air in the membrane pores. Preliminary experiments on water deaeration using a commercial membrane contactor were carried out. The dynamics of membrane and air gap deaeration using deaerated feed water were studied experimentally. A module test facility was equipped with a feed water deaeration setup. Full-scale spiral-wound modules with a membrane area of 5 and 10m2 have been used for comprehensive experimental studies on the use of deaerated feed water. The impact of deaeration at different pressures, feed flow rates, feed water salinities and temperature levels is quantified and discussed. A comparison of module operation with and without preceding feed water deaeration is given. The experimental results include module output rate, thermal energy demand, thermal efficiency and an estimation of the electrical energy demand. In all experiments, the beneficial effect of feed water deaeration can clearly be shown for total flux and thermal energy demand simultaneously. For selected operation points, the relative effect was identified to be more than 50% and the specific thermal energy demand dropped to values of less than 100kWhthm−3.

Numerical weather prediction as a surrogate for climate observations in practical applications

Climate data is used in many practical applications including energy demand estimations for heating and cooling, agricultural applications, risk assessment, and many more. The required climate data is only available if meteorological observations exist at a given location. In this study, the possibility of replacing long observational records with a few years of numerical weather forecast data is investigated for practical applications requiring temperature data. Observational data from 1980–2010, measured at 700 weather stations in Central Europe are used together with model forecasts of the years 2008–2010. Depending on the station, forecast data capture 90–110% of the standard deviation observed for daily mean and maximum temperatures and slightly less for minimum temperature. Heating and cooling degree days can be estimated with an error of 5–15% in climates where they have a relevance. Based on model data, maps of heating and cooling degree days are computed and the regional uncertainties are quantified using the observational data. The results suggest that numerical weather forecast data can be used for certain practical applications, either as a surrogate of observational data or for quite reliable estimates in locations with no observations.

Innovative CO2 separation of biogas by polymer resins: Operation of a continuous lab‐scale plant

Upgrading biogas allows for the injection of biomethane into the natural gas grid and thus a decentralized use. Since the currently available techniques have a high energy demand, there is a high potential to improve biogas upgrading. Innovative CO2 separation of biogas by the use of polymer resins can reduce the energy demand, the capital expenditure, and the operational costs. In this study, we show the ability of polymer resin to selectively adsorb CO2. Desorption tests showed the potential for continuous use of the resin. In a continuous lab‐scale plant, numerous variations of process parameters were carried out and optimization possibilities were demonstrated. Methane purity up to 98% was achieved. The favorable estimated energy demand indicates the great potential of the demonstrated improved process.

Forecasting Energy Demand in Iran Using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) Methods

The main objective of this research is to estimate energy demand in Iran using intelligence techniques based on the structure of Iran's industry and economic conditions. This study develops a scenario to analyze energy consumption and makes future projections based on particle swarm optimization (PSO) and genetic algorithm (GA) methods. The models are developed in two forms (exponential and linear) and applied to forecast energy demand in Iran. PSO and GA demand estimation models are developed to estimate the future energy demand values based on population, gross domestic product, import, and export data. Energy consumption in Iran from 1981–2005 is considered as the case of this study. The available data is partly used for finding the optimal, or near optimal, values of the weighting parameters (1981–1999) and partly for testing the models (2000–2005). Energy demand in Iran is forecasted up to year 2030.

Estimating Transport Energy Demand Using Ant Colony Optimization

This study proposes a heuristic algorithm based on ant colony optimization for estimating the transport energy demand (TED) of Turkey using gross domestic product, population, and vehicle-km. Three forms of the improved ant colony optimization transport energy demand estimation (IACOTEDE) models are used for improving estimating capabilities of TED models. Performance of IACOTEDE is compared with the Ministry of Energy and Natural Resources (MENR) projections. Sensitivity analysis is also carried out for testing the effects of the parameters. The quadratic form provided a better-fit solution to the observed data, and it underestimates Turkey's TED by about 28% less than the MENR projection in year 2025. Thus, it may be used with a highest correlation coefficient and considerably lower relative error according as the MENR projection in the testing period. It is also expected that this study will be helpful in developing highly applicable and productive planning for transport energy policies.

Evaluating daylighting potential and energy efficiency in a classroom building

In this paper, we analyze the possibility of using daylight as the main source of light for learning and teaching activities in a classroom building of the Universidade Tecnológica Federal do Paraná, in Curitiba, Brazil. Beginning with a survey on light bulb and luminaire types used in the building and on the potential of daylighting in such environments, six different classroom types with different solar orientations were selected for analysis. The research methods included the estimation of daylighting potential from obtained daylight coefficients (DCs) for periods of the year and sky conditions, other than those of the onsite measurements; the evaluation of conformity of the lighting system to the Brazilian RTQ Code (Regulamento Técnico de Qualidade), an energy consumption rating system, aimed at reducing electric energy consumption in office buildings; the estimation of electric energy demand for a combined use of daylighting and lighting system; the proposal of an efficient lighting system; and cost assessment using payback analysis. Results suggest energy savings of up to 94% when integrating daylight with the artificial lighting system, a payback within the range of 22–60 months for a complete substitution of the existing lighting system for a more efficient one.

A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey

This paper proposes a new hybrid method (HAP) for estimating energy demand of Turkey using Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). Proposed energy demand model (HAPE) is the first model which integrates two mentioned meta-heuristic techniques. While, PSO, developed for solving continuous optimization problems, is a population based stochastic technique; ACO, simulating behaviors between nest and food source of real ants, is generally used for discrete optimizations. Hybrid method based PSO and ACO is developed to estimate energy demand using gross domestic product (GDP), population, import and export. HAPE is developed in two forms which are linear (HAPEL) and quadratic (HAPEQ). The future energy demand is estimated under different scenarios. In order to show the accuracy of the algorithm, a comparison is made with ACO and PSO which are developed for the same problem. According to obtained results, relative estimation errors of the HAPE model are the lowest of them and quadratic form (HAPEQ) provides better-fit solutions due to fluctuations of the socio-economic indicators.

Optimization of Power-Intensive Energy Systems with Carbon Capture

A systematic methodology for the synthesis of power systems has been developed, which explores the interactions among the equipment as well as the processes in power and utility systems. Also, the optimization is able to study the integration of CO2 capture processes with power systems for minimizing site-wide fuel consumption. Typical operating conditions for precombustion as well as postcombustion CO2 capture have been applied to estimate energy demand within the CO2 capture process. The power system is designed not only to meet the electricity and shaft power demand from the background process but also to satisfy the additional energy requirement incurred by CO2 removal. With the energy integration for the overall systems, the fuel consumption could be reduced, and operating cost, minimized. A case study is presented to demonstrate the usefulness and effectiveness of the proposed methodology for the design of energy and power systems in liquid natural gas (LNG) plants, under a carbon-constrained busine...

Aerobic and Anaerobic Energy During Resistance Exercise at 80% 1RM

The present study investigated the accumulated oxygen deficit (AOD) method to assess the energy cost in resistance exercises (RE). The aim of the study was to evaluate the aerobic and anaerobic energy release during resistance exercises performed at 80% 1-RM in four exercises (half squat, bench press, triceps extension and lat pull down), as well as the accuracy of its estimation. The sample comprised 14 men (age = 26.6 ± 4.9 years; height = 177.7 ± 0.1 cm; body mass = 79.0 ± 11.1 kg; and estimated fat mass = 10.5 ± 4.6%). Test and re-test of 1-RM were applied to every exercise. Low-intensity bouts at 12, 16, 20, and 24% of 1-RM were conducted. Energy cost was then extrapolated to 80% 1-RM exhaustive bout and relative energy contribution were assessed. By utilizing the AOD method, the results of the present study suggest a great proportion of anaerobic metabolism during exercise at 80% 1-RM in the four RE that were analyzed: Bench press = 77,66±6,95%; Half squat = 87,44±6,45%; Triceps extension = 63,91±9,22%; Lat pull down = 71,99±13,73 %. The results of the present study suggest that AOD during resistance exercises presents a pattern that does not match the reports in the literature for other types of exercise. The accuracy of the total energy demand estimation at 80% 1-RM was acceptable in the Bench press, in the Triceps extension and in the Lat pull down, but no in the Half squat. More studies are warranted to investigate the validity of this method in resistance exercise.

Total Energy Demand Estimation in Iran Using Bees Algorithm

Many studies are performed by researchers to estimate energy demand but the bees algorithm (BA) technique has never been used for such a study. This paper presents application of the BA technique to estimate total energy demand in Iran, based on socioeconomic indicators. The BA demand estimation models (BA-DEM) are first developed in two forms (exponential and linear) in order to estimate energy demand based on population, gross domestic product, import and export data. Then, two scenarios are designed for forecasting each socioeconomic indicator in the years 2006–2030. Finally, these two scenarios are used to forecast energy demand up to year 2030 using demand estimation models. Energy consumption in Iran from 1981–2005 is considered as the case of this study. The available data is partly used for finding the optimal, or near optimal, values of the weighting parameters (1981–1999) and partly for testing the models (2000–2005). In order to show the accuracy of the BA, obtained results of BA-DEM models are compared with particle swarm optimization and genetic algorithm demand estimation models which are developed for the same problem.

Estimation of elasticity price of electricity with incomplete information

The sharp increase in energy prices and growing concern for environmental issues, among other things, are behind the renewed interest in energy demand estimation. However, there is scarce academic literature that takes real situation of energy suppliers into account: high quality but incomplete data. In this paper, we propose a useful and rather simple instrument for estimating electricity demand with the incomplete and/or imperfect data currently available to suppliers. In particular, using real expenditure and consumption data on electricity, we employ a model of random effects for panel data to estimate residential and industrial electricity demand in Spain.