There are several ways to attempt to model a building and its heat gains from external sources as well as internal ones in order to evaluate a proper operation, audit retrofit actions, and forecast energy consumption. Different techniques, varying from simple regression to models that are based on physical principles, can be used for simulation. A frequent hypothesis for all these models is that the input variables should be based on realistic data when they are available, otherwise the evaluation of energy consumption might be highly under or over estimated. In this paper, a comparison is made between a simple model based on artificial neural network (ANN) and a model that is based on physical principles (EnergyPlus) as an auditing and predicting tool in order to forecast building energy consumption. The Administration Building of the University of Sao Paulo is used as a case study. The building energy consumption profiles are collected as well as the campus meteorological data. Results show that both models are suitable for energy consumption forecast. Additionally, a parametric analysis is carried out for the considered building on EnergyPlus in order to evaluate the influence of several parameters such as the building profile occupation and weather data on such forecasting. (C) 2008 Elsevier B.V. All rights reserved.

Este trabalho utiliza indicadores de energia para caracterizar unidades consumidoras da Universidade de São Paulo pelo comportamento no uso da eletricidade e estabelecer prioridades de atuação para gestão dos recursos e ações de eficiência energética. Os indicadores foram aplicados na base de dados de consumo de energia extraídos da ferramenta de gestão da USP, o SISGEN, juntamente com as informações físicas, de ocupação, principais usos finais e custos de utilização da energia, em 48 unidades da Cidade Universitária Armando de Sales Oliveira. Os resultados foram utilizados para elaborar listas de prioridades de atuação para cada um dos indicadores e, a partir da análise desses resultados foi possível identificar tendências e semelhanças de comportamento no uso da energia elétrica no campus. Visando auxiliar na análise dos resultados, utilizou-se de uma classificação prévia por especialidades de ensino ou atividades desenvolvidas e, dois períodos distintos, cujo comportamento tende a sofrer alterações, ou seja, letivo e férias escolares. De posse de todas as informações e resultados elaborou-se uma classificação final no intuito de auxiliar os gestores na priorização das ações de gestão dos recursos e eficiência energética.; The work here presented uses indicators to classify energy consumption units at Universidade de São Paulo by consumption behavior and also to prioritize actions on resources management and establish energy efficiency actions. Such indicators have been applied to the energy consumption data base from the USP management tool...

A previsão do consumo de energia elétrica do Brasil é muito importante para os órgãos reguladores do setor. Uma série de metodologias têm sido utilizadas para a projeção desse consumo. Destacam-se os modelos de regressão com dados em painel, modelos de cointegração e defasagem distribuída, modelos estruturais de séries temporais e modelos de Box & Jenkins de séries temporais, dentre outros. Neste trabalho estimar-se um modelo de previsão do consumo comercial, industrial e residencial de energia brasileiro por meio de modelos SARMAX. Nesses modelos o consumo de energia pode ser estimado por meio de uma regressão linear múltipla considerando diversas variáveis macroeconômicas como variáveis explicativas. Os resíduos desse modelo são explicados por meio de um modelo de Box & Jenkins. Neste estudo realiza-se uma pesquisa bibliográfica sobre fatores que influenciam no consumo de energia elétrica e levantam-se variáveis proxies para prever este consumo no Brasil. Utiliza-se uma base de dados mensal no período entre Janeiro de 2003 e Setembro de 2010 para construção de cada um dos três modelos de previsão citados. Utilizase uma amostra de validação de Outubro de 2010 até Fevereiro de 2011. Realiza-se a avaliação dos modelos estimados em termos de adequação às premissas teóricas e ao desempenho nas medidas de acurácia MAPE...

No presente trabalho se analisou o consumo energético de uma moradia social, para isto se calculou a energia incorporada nos materiais construtivos utilizados no projeto da moradia analisada e o consumo energético provocado pelo comportamento térmico da envolvente térmica da edificação estudando como a mudança dos materiais construtivos como: telhados e paredes modificam o valor da energia incorporada e o consumo de energia elétrica pelo comportamento térmico, uma vez que se considera possível que o consumo energético por motivos térmico seja maior que a energia incorporada. Para o calculo da energia incorporadados materiais construtivos que compõem a envolvente, foram utilizados coeficientes de energia incorporada propostos por Tavares, (2006), Graf; Tavares, (2010), Lobo, (2010). A pesquisa foi enfocada ao setor habitacional por ser um dos setores que apresentam maior consumo de energia, enfocando-se especificamente à moradia social. A metodologia para avaliar o consumo energético da edificação no período de análise, se divide em quatros etapas: 1) Quantificação de materiais da envolvente 2) Cálculo de energia incorporada 3) Cálculo de gasto energético nos períodos de 1 e 40 anos realizado por meio de simulações no software Energy Plus 4) Cálculo dos coeficientes de correlação das variáveis.Tomaram-se as cidades de São Carlos...

Nowadays, reducing energy consumption is one of the highest priorities and biggest challenges faced worldwide and in particular in the industrial sector. Given the increasing trend of consumption and the current economical crisis, identifying cost reductions on the most energy-intensive sectors has become one of the main concerns among companies and researchers. Particularly in industrial environments, energy consumption is affected by several factors, namely production factors(e.g. equipments), human (e.g. operators experience), environmental (e.g. temperature), among others, which influence the way of how energy is used across the plant. Therefore, several approaches for identifying consumption causes have been suggested and discussed. However, the existing methods only provide guidelines for energy consumption and have shown difficulties in explaining certain energy consumption patterns due to the lack of structure to incorporate context influence, hence are not able to track down the causes of consumption to a process level, where optimization measures can actually take place. This dissertation proposes a new approach to tackle this issue, by on-line estimation of context-based energy consumption models, which are able to map operating context to consumption patterns. Context identification is performed by regression tree algorithms. Energy consumption estimation is achieved by means of a multi-model architecture using multiple RLS algorithms...

We investigated Granger-causality in the frequency domain between primary energy consumption/electricity consumption and GDP for the US by employing approach of Lemmens et al. (2008) and covering the period of January, 1973 to December, 2008. We found that causal and reverse causal relations between primary energy consumption and GDP and electricity consumption and GDP vary across frequencies. Our unique contribution in the existing literature lies in decomposing the causality on the basis of time horizons and demonstrating bidirectional the short-run, the medium-run and the long-run causality between GDP and primary energy consumption/electricity consumption and thus providing evidence for the feedback hypothesis. These results have important implications for the US for planning of the short, the medium and the long run energy and economic growth related policies.

Daylight is renewable energy and abundant source. When used to balance the entrance of daylight and heat load within the room, brings benefits as the reduction in energy consumption. In commercial buildings lighting and air conditioning are responsible for larger proportion of final energy consumption. These buildings have great potential to reduce this consumption through balanced daylighting and air conditioning, avoiding waste. To find this balance, the computational simulation programs have been presented as a good alternative to estimate the consumption of an integrated way. This study aimed to evaluate the influence of daylighting in reducing the consumption of electric energy, whereas also, the use of air conditioning for models based on commercial buildings in Maceió. For this evaluation three models of different proportions, with a focus on depth and two sizes of different apertures were simulated in programs EnergyPlus and TropLux. In a first stage illuminances useful generated by computational simulations were evaluated where also the laminated reflective glass was used. In a second stage, the consumptions of daylighting and air conditioning were simulated integrated, considering a system of artificial lighting control. According to the analyzes were observed the illuminances useful are found in higher concentration in models with larger apertures...

The objectives of this work were to charac- terize the energy consumption and the heat transfer process by the determination of the convective heat transfer coefficient (h) of passion fruit pulp contained in high-density polyethylene (HDPE) boxes and frozen in two conditions: without and with airflow induction, which was achieved through the installation of obsta- cles. To determine the convective heat transfer coeffi- cients, HDPE boxes containing passion fruit pulp (contained in polyethylene bags) were interspersed with boxes containing metal tanks filled with low freezing point solutions. Three types of solutions were used: ethylene glycol, propylene glycol, and ethanol. The airflow induction under the stacks of passion fruit pulp provided higher h values than without airflow induction. The calculated average values and standard deviation were 6.340±0.87 W/m 2 °C, respectively, without air- flow induction and 8.419±1.39 W/m 2 °C with airflow induction. The average reduction of the freezing time was 25 % for the boxes located at the top and 20 % in the base of the stack. This proved that directing the airflow under the stacked product promoted more uniform and efficient heat transfer. The analysis of the electrical parameter measurements revealed an approximate de- crease of 16.7 % in energy consumption due to the reduction of the freezing time...

The analysis of household energy consumption patterns is critical for evaluating public mechanisms, such as subsidies and social tariffs that aim to provide lower income earners with better access to energy sources. This paper focuses on Mexican households to analyze the relations between their levels of income, consumption of different forms of energy, and the role played by different household characteristics. Using microdata from the Mexican Income Expenditure Surveys, the paper first relate income and energy expenditure to determine the shape of this relation. It then applies OLS and Tobit models to determine how income levels affect energy consumption in relation to other covariates. The results show a positive relation for income deciles and energy consumption and some household characteristics -- pointing to differentiated mechanisms for improving energy use.

CIVINS; Approved for public release; distribution is unlimited; As of October 1, 2008, the Department of the Navy inserted the requirement that all new buildings constructed for the United States Navy and United States Marine Corps would have an additional requirement to become Leadership in Energy and Environmental Design (LEED) Silver certified by the United States Green Building Council (USGBC). The goal of this effort is in compliance with Executive Order (EO) 13423, which provides that all Government departments must reduce energy consumption by 30% by 2015. The objective of this research is to find if the eleven buildings for the United States Navy that have already received a LEED certification or higher, have achieved the expected energy consumption savings in comparison to other similar non-LEED certified facilities. To accomplish this effort, these buildings have been compared to other United States Navy and Marine Corps commercial buildings of comparable size, usage, and within the same region as chosen by the respective Public Works Departments. The data being compared for this study will be electricity and water consumption, based upon what could be received from the current building's meters. Additionally, these LEED certified buildings will be compared to the national averages for electricity consumption as expressed by the Commercial Building Energy Consumption Survey (CBECS) to find if any energy savings is achieved to buildings of similar size.; Contract number: N00244-00-G-0004.; CIVINS

Twenty per cent of all energy consumed in New England and ten per cent of all energy consumed in the United States is consumed in home heating. This paper reports on an effort to ascertain the major factors affecting the consumption of home heating oil. Three general classes of factors are analyzed: (1) physical and occupant characteristics (number of rooms, number of occupants, number of stories, amount of insulation, income level, etc.); (2) external (price, shortage awareness, weather); and (3) behavioral and physical changes (change in temperature settings, change in insulation, change in oil burner, etc.). The study is based on four data series: (1) actual monthly home heating oil consumption data on 8000 suburban homeowners in suburban Boston; (2) questionnaire responses from 2000 homeowners on their homes' physical and occupant characteristics, as well as changes in physical and occu- pant behavioral characteristics between 1972 and 1975; (3) monthly weather data; and (4) heating oil price data. The data is associated with the years from 1972 through 1975, a period in which marked price changes, shortages, and behavioral changes occurred, hence providing an opportunity to study the effects of these various events. Three models are central to the study: Model I. A cross-sectional model that depicts consumption per degree-day as a function of physical and occupant characteristics of a home. Model II. A time series regression model that establishes consumption per degree-day as a function of price and consumer awareness of an energy shortage. Model III. A cross-sectional regression model that attempts to explain change in consumption per degree-day from one year to the next as a function of specific conservation actions such as temperature resetting...

Input-output analysis has been proven to be a powerful instrument for estimating embodied (direct plus indirect) energy usage through economic sectors. Using 9 economic input-output tables of years 1987, 1990, 1992, 1995, 1997, 2000, 2002, 2005, and 2007, this paper analyzes energy flows for the entire city of Beijing and its 30 economic sectors, respectively. Results show that the embodied energy consumption of Beijing increased from 38.85 million tonnes of coal equivalent (Mtce) to 206.2 Mtce over the past twenty years of rapid urbanization; the share of indirect energy consumption in total energy consumption increased from 48% to 76%, suggesting the transition of Beijing from a production-based and manufacturing-dominated economy to a consumption-based and service-dominated economy. Real estate development has shown to be a major driving factor of the growth in indirect energy consumption. The boom and bust of construction activities have been strongly correlated with the increase and decrease of system-side indirect energy consumption. Traditional heavy industries remain the most energy-intensive sectors in the economy. However, the transportation and service sectors have contributed most to the rapid increase in overall energy consumption. The analyses in this paper demonstrate that a system-wide approach such as that based on input-output model can be a useful tool for robust energy policy making.

Dissertação de Mestrado em Engenharia Informática; The use of powerful mobile devices, like smartphones, tablets and laptops, are changing the way programmers develop software. While in the past the primary goal to optimize software was reducing the run time, nowadays there is a growing awareness of the need to reduce energy consumption. In this thesis we present a combination of techniques to detect anomalous energy consumption in Android applications, and to relate it to their source code. The idea is to provide applications developers with techniques and tools to locate in the source code of the application the code fragments that are responsible for high energy consumption. Thus, we present a model for energy consumption for the Android ecosystem. The model is then used as an API to monitor the application execution. To relate program execution and energy consumption to the application source code, the code is first instrumented with calls to the API of the model. To execute that (instrumented) application, we use a testing framework for Android. Finally, we use a statistically approach, based on fault-localization techniques, to localize abnormal energy consumption in the source code.; O uso de dispositivos móveis, como smartphones...

Residential energy consumption is becoming an ever-growing sector, in 2012 accounting for 21.2% of total consumed energy in the United States. People have been focused on exploring potential strategies to lower the associated energy consumption, including energy efficiency improvement and policy adjustment. However, in the absence of detailed breakdown of energy consumption, it is hard to effectively apply energy efficiency upgrades, meaning a necessity of getting a deep understanding of the energy consumption estimates. To achieve the breakdown of energy consumption, this project primarily focuses on the individual home energy consumption, through estimating the energy consumed in space heating, space cooling, water heating, and appliances. In particular, multiple-regression method is employed to construct the prediction model, and Residential Energy Consumption Survey (RECS) is used as the source data, which includes more than 12000 samples and has satisfactory data reliability. As a result, many select factors are proven to be closely associated with certain energy end use. For instance, space heating/cooling energy consumption is greatly driven by the housing unit square feet, and a bigger house would result in more energy consumed in the heating and cooling sector...

There has been a growing interest in discovering the human effects on the environment and energy consumption in recent decades. It is estimated that the share of energy consumed in transportation and housing systems are around 20 and 30 percent of total energy consumption respectively. Furthermore, the residential greenhouse emissions depend on urban form and structure. This paper explores the effects of urban features on residential energy consumption at neighborhood level using data collected through household questionnaire (n=140). Two residential districts in metropolitan Shiraz, south of Iran, were selected as case study areas. Different features of two areas were compared including building density, typology, housing location, parcel size, floor area and construction materials. Ordinary linear regression was used to discover the impact of explanatory variables on energy consumption. It was found that some physical variables such as parcel size, setback and number of floors played significant roles in explaining the variances exist in energy use level. The results can be used by governmental agencies to modify land use policies and subdivision rules in hope of saving energy and achieving a sustainable community.

In view of the close association between energy and economic growth, South Africa's aspirations for higher growth, more energy is required; formulating a long-term economic development plan and implementing an energy strategy for a country /industry necessitates establishing the correct relationship between energy and the economy. As insufficient energy or a lack thereof is reported to be a major cause of social and economic poverty, it is very important to select a model to forecast the consumption of energy reasonably accurately. This study presents techniques based on the development of multilayer perceptron (MLP) and radial basis function (RBF) of artificial neural network (ANN) models, for calculating the energy consumption of South Africa's industrial sector between 1993 and 2000. The approach examines the energy consumption in relation to the gross domestic product. The results indicate a strong agreement between model predictions and observed values, since the mean absolute percentage error is below 5%. When performance indices are compared, the RBF-based model is a more accurate predictor than the MLP model.

This paper investigates the causal relationship between energy consumption and real GDP in 14 Sub-Saharan African countries over the period 1971-2004. The results of panel co-integration tests showed that energy consumption and real GDP do not have a stable long-run equilibrium relationship. We find that for all members of the panel, there is homogenous causality from energy consumption to real GDP and vice versa. This bi-directional causality supports the feedback hypothesis.

This study reassesses the causal relationships between energy consumption and economic growth in 18 Sub-Saharan Africa countries over the period 1980-2011. The Panel Unit Root Test results show that variables (both exogenous and endogenous) are stationary at their first difference with individual effects and individual linear trends, while the results of panel co-integration tests show that energy consumption and economic growth do have a stable long-run equilibrium relationship. There is unidirectional causality from energy consumption to economic growth in East and the Southern Africa Sub-region, which supports the growth hypothesis. As a result, the related authorities in the regions should take a special interest in different sources of energy and invest more in this sector, make suitable policies in this regard and find new alternative and cheap sources of energy. But, there is no causality between energy consumption and economic growth in Central and the West Africa Sub-region, which is in line with the neutrality hypothesis. In other words, both energy consumption and economic growth are neutral with respect to each other. Our results confirm the inconclusive nature of a causality relationship between energy consumption and economic growth.

This paper examines the energy consumption, supply and resources of some of the countries in the Southern African Development Community (SADC) in 2005, the base year for this analysis. The region is rich in energy resources and currently enjoys relatively stable and affordable electricity. Except in the case of Botswana, Mauritius, Namibia and South Africa, final energy demand is dominated by the residential sector in the form of biomass. Energy consumption or final energy demand in Angola, Botswana, Mozambique, South Africa, Tanzania, Zambia and Zimbabwe is projected to 2030 using a Long Range Energy Alternatives Planning (LEAP) model in a 'business as usual' scenario, the other countries being left out because of poor quality data. The projections are carried out by relating historic sectoral GDP and population growth in each country to energy demand and then using the historical link and the projections of these drivers to project energy demand. The analysis under this 'business as usual' scenario seems to indicate that we can expect to see a large increase in consumption in these countries, especially in the form of biomass and electricity. In both cases, supply is a concern. Having established what the present resources are; what some of the supply elements are currently in place; what the base-year demand is; and some basic relationships between demand and socio-economic drivers...

This paper investigates energy consumption patterns by households and individuals during travel on a typical day. A methodology is developed to estimate trip-by-trip energy consumption using standard 24-hour travel survey data, and applied to the Nelson Mandela Metropolitan Area using their 2004 household travel survey. Baseline energy consumption patterns by different modes, times of day, and user groups are established. Across the population, energy use is very skewed: 20% of people consume about 80% of transport energy, mainly due to the disproportional contribution of car use to energy expenditure. We then estimate a disaggregate vehicle ownership model and link it to a model of household transport energy consumption to explore the underlying socio-economic and land use variables driving energy consumption. Land use factors (especially job accessibility) significantly affect energy use, but do so differently for low and for high-income households, suggesting that accessibility-enhancing land use and transport measures could have unintended consequences for overall energy and environmental management