Prediction Of Energy Use Research Articles

Applying artificial neural networks and multi-objective genetic algorithm to modeling and optimization of energy inputs and greenhouse gas emissions for peanut production

This study was conducted in order to model energy consumption and greenhouse gas emissions for peanut production in Guilan province of Iran using artificial neural network (ANN). Also, the multi-objective genetic algorithm was used for optimization of energy inputs and GHG emissions in the region. Data were randomly collected from 120 farms in Astaneh Ashrafiyeh city with face to face questionnaire method. The results illustrated that the total energy consumption and the average yield were calculated as 19248.04 MJ ha-1 and 3488.39 kg ha -1 , respectively. Moreover, the results showed that the share of chemical fertilizers (mainly nitrogen) and diesel fuel energy to the total energy input were the highest. Also, the energy used efficiency ratio calculated as 4.53. The results of GHG emissions analysis showed the total GHG emissions were 571.18 kgCO2eq. ha -1 and the diesel fuel has the main reasonable of GHG emissions in peanut production. In this study, several direct and indirect factors have been identified to create a model based on ANN to predict energy use and GHG emissions in peanut production. The ANN model with 9-22-2 structure was capable of predicting the peanut yield and GHG emissions. Moreover, the results of the best topology showed that R 2 was 0.994 and 0.999, RMSE was 0.076 and 0.003 and MAPE was 0.174 and 0.009 for peanut yield and GHG emissions, respectively. The results of optimization indicated the total energy consumption and GHG emissions generation was calculated about 6888 MJ ha -1 and 159.08 kgCO2eq. ha -1 , respectively. The total GHG emissions reduction was found to be 412.09 kgCO2eq. ha -1 in optimal generation toward present farms.

Influence of thermophysiology on thermal behavior: the essentials of categorization

Predicted energy use of dwellings often deviates from the actual energy use. Thermoregulatory behavior of the occupant might explain this difference. Such behavior is influenced by thermal sensation and thermal comfort. These subjective ratings in turn are linked to physiological parameters such as core and skin temperatures. However, it is unclear which physiological parameters best predict thermoregulatory behavior.The objective of this research was to study physiological parameters that potentially can be used to predict thermoregulatory behavior. Sixteen healthy females (18–30years) were exposed to two dynamic temperature protocols: a gradual increase (+4K/h, ranging from 24°C to 32°C) and a gradual decrease in ambient temperature (−4K/h, ranging from 24°C to 16°C). During the experiments physiological responses, thermal sensation, thermal preference and the intention of thermoregulatory behavior were measured. Thermal sensation is highly correlated with thermal preference (r=−0.933, P<0.001). The skin temperature of the wrist best predicts thermal sensation (R2=0.558, P<0.001) and therefore seems useful as a physiological parameter to predict the intention of thermoregulatory behavior. When the subjects are categorized based on their thermal sensation votes, more precise predictions of thermal sensation can be made. This categorization therefore can be of value for the determination of the actual energy use of occupant in dwellings.

Specific energy consumption values for various refrigerated food cold stores

Two benchmarking surveys were created to collect data on the performance of chilled, frozen and mixed (chilled and frozen stores operated from a single refrigeration system) food cold stores with the aim of identifying the major factors influencing energy consumption. The volume of the cold store was found to have the greatest relationship with energy use with none of the other factors collected having any significant impact on energy use. For chilled cold stores, 93% of the variation in energy was related to store volume. For frozen stores, 56% and for mixed stores, 67% of the variation in energy consumption was related to store volume. The results also demonstrated the large variability in performance of cold stores. This was investigated using a mathematical model to predict energy use under typical cold store construction, usage and efficiency scenarios. The model demonstrated that store shape factor (which had a major impact on surface area of the stores), usage and to a lesser degree ambient temperature all had an impact on energy consumption. The work provides an initial basis to compare energy performance of cold stores and indicates the areas where considerable energy saving are achievable in food cold stores.

Revealing occupancy patterns in an office building through the use of occupancy sensor data

Energy simulation programs like DOE-2 and EnergyPlus are tools that have been proven to aid with energy calculations to predict energy use in buildings. Some inputs to energy simulation models are relatively easy to find, including building size, orientation, construction materials, and HVAC system size and type. Others vary with time (e.g. weather and occupancy) and some can be a challenge to estimate in order to create an accurate simulation. In this paper, the analysis of occupancy sensor data for a large commercial, multi-tenant office building is presented. It details occupancy diversity factors for private offices and summarizes the same for open offices, hallways, conference rooms, break rooms, and restrooms in order to better inform energy simulation parameters. Long-term data were collected allowing results to be presented to show variations of occupancy diversity factors in private offices for time of day, day of the week, holidays, and month of the year. The diversity factors presented differ as much as 46% from those currently published in ASHRAE 90.1 2004 energy cost method guidelines, a document referenced by energy modelers regarding occupancy diversity factors for simulations. This may result in misleading simulation results and may introduce inefficiencies in the final equipment and systems design.

Integrating probabilistic methods for describing occupant presence with building energy simulation models

This paper presents a method for developing a probabilistic-based occupancy model that focuses on occupants’ long vacancy activities (greater than 1 week) and other potential building underutilization that is further integrated with a building energy simulation model. The combined model is then applied toward an existing Leadership in Energy and Environmental Design (LEED) certified military dormitory and later compared with corresponding values from the energy model's original prediction as well as actual building energy data. The occupancy model simulates annual building occupancy rates comprised of weekly values based on the frequency, duration, and seasonality of occupants’ long vacancy activities. The energy model uses the simulated occupancy rates to yield the building's predicted range of energy performance. Applying the combined model to the existing LEED building resulted in an improved, predicted Energy Use Intensity (EUI) mean value of 612MJ/m2 as compared to the original model and actual EUI values of 691 and 590, respectively. While the model also demonstrated its utility in describing the change in predicted performance over a range of probabilities associated with certain long vacancy activities, efforts to incorporate other occupant behavior-related aspects such occupant schedules and thermal set points could further improve modeling efforts.

Energy Use and Greenhouse Gas Emissions in Selected Hindu Kush–Himalayan Countries

This article discusses historical patterns of energy supply, electricity generation, and sectoral energy consumption as well as the emission of energy-related greenhouse gases during 1995–2008 in 5 Hindu Kush–Himalayan countries, Bangladesh, Bhutan, India, Nepal, and Pakistan, and reviews major studies that predict energy use and greenhouse gas emissions during 2005–2030 in the absence of climate policy interventions. It presents a range of energy use and greenhouse gas emission projections for the selected countries as a whole, and for the Hindu Kush–Himalayan areas in those countries for 2030. This study shows a need to establish a spatially disaggregated (district-level) energy database in the Hindu Kush–Himalayan countries to enable more accurate estimates of energy use and associated greenhouse gas emissions in the region.

Calculating the greenhouse gas emissions of water utilities

The objective of this study was to develop and test a user‐friendly greenhouse gas (GHG) emissions accounting tool for water utilities. The tool was designed to assist utilities in calculating GHG emissions for the purposes of complying with future regulation and reporting requirements, providing information for city and state climate action plans, and fulfilling voluntary carbon emissions goals. For utilities lacking accurate energy use data, equations were developed to predict energy use for three phases of water production: raw water collection, water treatment, and finished water distribution. The tool was tested by seven Georgia, North Carolina, and South Carolina utilities, which had an average carbon intensity of 1,240 kg carbon dioxide equivalents (CO2‐eq)/mil gal when scope 1 and 2 emissions were evaluated. However, the averages ranged from 550 to 2,190 kg CO2‐eq/mil gal, depending on the electrical grid used.

A comprehensive analysis of the impact of occupancy parameters in energy simulation of office buildings

The commercial building sector has become the focus of many governmental energy reduction initiatives to achieve more sustainable development. Reducing building energy use starts by improving the design of buildings. To this end, energy modeling and simulation tools are used during the design phase to predict energy use and help designers choose and size the different building systems. Large discrepancies are however being observed between predicted and actual building energy performances. In order to determine the sources of errors and improve these predictions, the sensitivity of energy models to different input parameters needs to be evaluated. Studies in literature have extensively evaluated the sensitivity of models to the buildings’ technical design parameters. However, none considered the parameters related to the energy consumption behavior of occupants, leaving their impact on energy modeling unknown. This paper presents a comprehensive sensitivity analysis study performed on the occupancy behavioral parameters of typical office buildings of different size and in different weather zones. Significant sensitivity levels were observed, varying according to both building size and weather conditions. The highest sensitivity was obtained when varying the ‘heating temperature set point’ parameter in small-size buildings located in US weather zone 2 Dry.

BATS USE SHALLOW TORPOR TO MINIMISE COSTS

White-nose syndrome is a devastating fungal disease that is currently decimating the bat populations of North America. Specifically targeting hibernating bats, the disease appears to cause its victims to arouse from torpor more frequently, preventing them from conserving energy and leading to starvation. Craig Willis and Kristin Jonasson from the University of Winnipeg, Canada, explain that it is difficult to assess the energetic impact of this disease without realistic models of how hibernating bats use energy. So, the duo decided to monitor the skin temperatures of hibernating little brown bats to find out whether the hibernating animals optimise their use of torpor during hibernation and how well temperature measurements can be used to estimate the animals' energy consumption rates (p. 2141).Fitting temperature-sensitive radio transmitters to 22 bats hibernating in a small cave in central Manitoba, Canada, the duo monitored the bats' body temperatures for 3 months. Recording the lengthy periods when the bats became torpid (13.1 days on average), depressing their body temperatures, and the brief arousals when the animals returned their body temperatures to normal, Willis and Jonasson found that the duration of torpor varied significantly from animal to animal. They also found that the bats were torpid for an impressive 99.6% of the hibernation period.Next, the pair analysed the temperature recordings to find out whether the bats' age, sex or body condition affected their hibernation. They did not. However, when the scientists analysed the arousal periods, they noticed something unusual. Occasionally the bats dropped their body temperatures slightly during arousal and entered a period of shallow torpor. The duo suspects that the bats use these bouts – which they refer to as heterothermic arousal – to minimise the over all metabolic costs of hibernation. Finally, the team measured the masses of the tiny animals to estimate their energy use over the course of the hibernation, and found that their calculated predictions of energy use agreed well with the bats' mass loss, although the calculations slightly overestimated the energy consumption.Having assessed the mammal's hibernation energetics, Willis says, ‘Our findings will be important for understanding the energetic and survival implications for bats suffering from white-nose syndrome.’

Do not snoop my habits: preserving privacy in the smart grid

The recent deployment of smart grids has proven to bring numerous advantages in terms of energy consumption reduction in both homes and businesses. A more accurate measurement of up-to-date electricity necessities through smart meters utilization leads to an enhancement in the ability of monitoring, controlling and predicting energy use. Nevertheless, it has associated drawbacks related to the privacy of customers as well, since such management might reveal their personal habits and behavior, which electrical appliances they are using at each moment, whether they are at home or not, and so on. In this article we present a privacy enhanced architecture for smart metering aimed to tackle this threat by means of a new and novel protocol encrypting individual measurements while allowing the electricity supplier to access the aggregation of the corresponding decrypted values. The technique being used is named additively homomorphic encryption, and enables the direct connection and exchange of data between electricity suppliers and final users, while preserving the privacy of the latter.

Using real-time electricity data to estimate response to time-of-use and flat rates: an application to emissions

Using a generalized McFadden specification, we estimate the determinants of hourly response for the years 2006 through 2010 for all 16 standard retail customers who were on an optional real-time electricity rate offered by Duke Energy as of 2010, and provide a method to estimate how these customers would respond to time-of-use (TOU) and flat rates. We generalize the model to allow for inter-day response, as well as threshold prices, above which individual customer response may increase or decrease. With these inclusions, we find hourly elasticity for the group of customers to be as large as −0.7, larger than previous studies. We apply the method to examine a recent finding that time-differentiated rates could increase electric utility emissions. However, that result did not differentiate between real-time and TOU rates, and furthermore held energy use constant in comparing flat rates and time-differentiated rates. We perform a case study to examine emissions of SO2, NOx, Hg, and CO2 based on predicted energy use changes as well as for an energy-neutral case for real-time, TOU and flat rates. Employing energy use predictions from the model, increased energy use results in increased emissions in almost all cases. For the energy-neutral case, time-differentiated rates increase CO2 as compared to flat rates, and the TOU rate causes a larger increase than does real-time pricing. But both rates decrease other emissions in the majority of years, particularly SO2 In addition, time-differentiated rates reduce NOx potency by shifting it to non-daylight hours when conditions for the formation of smog are less favorable. Our application leads to the conclusion that the effect of the rates on emissions must consider total energy use as well as the shift from peak to off-peak. Furthermore, the predictions require consideration of the generating mix at a more detailed level than was contained in previous studies.

Validation of building energy modeling tools under idealized and realistic conditions

Building energy models provide valuable insight into energy use in commercial and residential buildings based on architecture, materials and thermal loads. They are used in the design of new buildings and retrofitting to increase the efficiency of older buildings. The accuracy of these models is crucial to reducing energy use in the US and building a sustainable energy future. In addition to the architecture and thermal loads, building energy models also must account for the effects of the building's occupants on energy use. Traditionally simple schedule based methods have been used to account for the effects of occupants. However, newer research has shown that these methods often result in large differences between the modeled and actual energy use of buildings. In this paper we discuss building energy models and their accuracy in predicting energy use. In particular we focus on the different types of validation methods which have been used to investigate the accuracy of building energy models and how they account for (or do not) the effects of occupants. We also review newer work on stochastic methods for estimating the effects of occupants on energy use and discuss the improvements necessary to increase the accuracy of building energy models.

Determination and modelling of energy consumption in wheat production using neural networks: “A case study in Canterbury province, New Zealand”

This study was conducted on irrigated and dryland wheat fields in Canterbury in the 2007–2008 harvest year based on an extensive process of data collection involving a questionnaire and interviews. Total energy consumption in wheat production was estimated at 22,566 MJ/ha. On average, fertilizer and electricity were used more than other energy sources, at around 10,654 (47%) and 4870 (22%) MJ/ha, respectively. The energy consumption for wheat production in irrigated and dryland farming systems was estimated at 25,600 and 17,458 MJ/ha, respectively. In this study, several direct and indirect factors have been identified to create an artificial neural networks (ANN) model to predict energy use in wheat production. The final model can predict energy consumption based on farm conditions (size of crop area), farmers’ social considerations (level of education), and energy inputs (N and P use and irrigation frequency), and it predicts energy use in Canterbury arable farms with an error margin of ±12% (±2900 MJ/ha). Furthermore, comparison between the ANN model and a Multiple Linear Regression (MLR) model showed that the ANN model can predict energy consumption relatively better than the MLR multiple model on the selected training set and validation set.

Evaluation of biomass and thermal energy utilization efficiency of oilseed Brassica (Brassica juncea) under altered microenvironments

Abstract Changes in within-canopy microenvironments may significantly influence the physiological response pattern of the crop eventually influencing biomass, seed yield and their heat utilization behavior. Therefore, energy use efficiency in oilseed crop Brassica juncea was evaluated to have better understanding of impact of microenvironments on plant characteristics. Linear and non-linear regression analysis showed significant and positive response of Brassica towards time variable leaf area index (LAI), dry biomass accumulation and their heat utilization efficiencies. Linear and third order polynomial regression models indicated 81 and 94% variation in LAI and dry biomass production respectively as a function of time (t) while about 85–93% variation in thermal energy use efficiency (THUE) was observed which might be due to differential thermal heat accumulation at its various phenological stages. Its quantification was done with second order regression equations using accumulated thermal heat (TI) (R 2 = 0.93∗∗) and solar energy (PTI) (R 2 = 0.85∗∗). THUE was predicted as 0.1616 × LAI - 0.0294; R 2 = 0.74∗∗ using TI and 0.0179 × LAI - 0.0044; R 2 = 0.75∗∗ using PTI and 0.0378 + 0.0012 × (DM) -3E-07 × (DM) 2 ; R 2 = 0.97∗∗ using TI and 0.0042 + 0.0001 × (DM)−3E-08 × (DM) 2 ; R 2 = 0.96∗∗ using PTI. The significant changes in seed yield heat use efficiency of the order 80–87% were attributed to differential thermal energy utilization capacities. We conclude that the prediction models developed from this study could be used in predicting energy use efficiency in other related species of oilseed Brassica .

Social Production of Desirable Space: An Exploration of the Practice and Role of Property Agents in the UK Commercial Property Market

The design, construction, and occupation of a sustainable built environment have been the focus of much research over recent years. Most notably, the focus has been on the modelling and prediction of energy use and carbon production during the design process together with the monitoring of carbon emissions during occupation. A second focus has highlighted the influence of occupant behaviour on carbon emissions. However, this overlooks the complex social interactions which together produce the built environment. There is room for seeking to unpack the social production of (un)sustainable buildings from design through construction to commissioning. These buildings significantly set the agenda for the behaviour of occupants whatever their level of carbon consciousness. In this paper we use the practices and role of property agents in the commercial property market as a window on the social production of (un)sustainable buildings.

The Energy and Cost Implications of the Renewable Energy Scheme at the Integrated Home for Older People, Plas y Môr, Llanelli, Wales

Plas y Môr is a ground-breaking project in the social housing sector in Wales. Completed in 2003, it provides 38 flats as well as care and a wide range of other facilities for the elderly. The building combines a number of renewable energy technologies within a highly insulated timber-frame, including passive and active solar technologies and biomass wood pellet fuelled boilers servicing a communal heating scheme. This paper briefly describes how the building works, and examines its performance to date. It compares predicted energy use against actual data from available utility bills over the period of operation of the scheme. Comparison with similarly sized, residential scheme show that Plas y Môr is performing well, despite a few initial setbacks.

Security and Privacy Challenges in the Smart Grid

Global electrical grids are verging on the largest technological transformation since the introduction of electricity into the home. The antiquated infrastructure that delivers power to our homes and businesses is being replaced with a collection of digital systems called the smart grid. This grid is the modernization of the existing electrical system that enhances customers' and utilities' ability to monitor, control, and predict energy use.

Using a Model of Human Energy Metabolism to Estimate Energy Costs of Turnover

The goal of this research is to use a computational model of nutrient digestion and metabolism in a human to predict energy use and quantify effects of increased futile cycling on energy metabolism. The model is composed of 6 state variables representing amino acids, body protein, visceral protein, glucose, triglyceride, and fatty acids. Differential equations represent carbohydrate, amino acid, and fatty acid uptake and output by tissues. The model estimates metabolizable energy, heat production (HP), respiratory quotient, P/O ratio, energy balance and glucose and fatty acid oxidation based on ATP creation and use, O2 use, CO2 production, heats of combustion, and nutrient flux. Protein deposition is a function of maximum genetic potential for protein accretion. Adipose deposition is a function of energy balance. To examine effects of increasing protein and adipose turnover, protein turnover increased from 2 to 14 %/day and adipose turnover increased from 1.5 to 3.0 %/day for a 70 kg male. Increasing protein turnover increased HP from 11.2 to 20.5 MJ/day, increased glucose oxidation from 1.88 to 2.29 mole/day, increased fatty acid oxidation from 0.132 to 0.873 mole/day and decreased body weight by 6 kg over a 30 day simulation. Increasing adipose turnover decreased HP from 10.8 to 10.7 MJ/day, decreased glucose oxidation from 2.18 to 2.04 mole/day, increased fatty acid oxidation from 0.0200 to 0.0543 mole/day, and decreased body weight by 4.5 kg over a 30 day simulation. Therefore the model is able to predict body weight loss due to increased heat production from increased energy use as a result of changes in muscle and adipose turnover. Since humans are genetically diverse with a range of basal metabolic rates, activity levels and diets, a computational model will quantify diversity and predict metabolic function for a wide range of individuals.

Mechanical efficiency of limb swing during walking and running in guinea fowl (Numida meleagris)

Understanding the mechanical determinants of the energy cost of limb swing is crucial for refining our models of locomotor energetics, as well as improving treatments for those suffering from impaired limb-swing mechanics. In this study, we use guinea fowl (Numida meleagris) as a model to explore whether mechanical work at the joints explains limb-swing energy use by combining inverse dynamic modeling and muscle-specific energetics from blood flow measurements. We found that the overall efficiencies of the limb swing increased markedly from walking (3%) to fast running (17%) and are well below the usually accepted maximum efficiency of muscle, except at the fastest speeds recorded. The estimated efficiency of a single muscle used during ankle flexion (tibialis cranialis) parallels that of the total limb-swing efficiency (3% walking, 15% fast running). Taken together, these findings do not support the hypothesis that joint work is the major determinant of limb-swing energy use across the animal's speed range and warn against making simple predictions of energy use based on joint mechanical work. To understand limb-swing energy use, mechanical functions other than accelerating the limb segments need to be explored, including isometric force production and muscle work arising from active and passive antagonist muscle forces.

Review and Analysis of an Energy Efficiency Incentive Program for Commercial Buildings

ABSTRACT A program with 13 participants provided reimbursement for improvements to decrease energy use largely in commercial and not-for-profit buildings, but also in two government buildings. Electricity and natural gas savings were determined by modeling the energy use by accounting for changes in weather for the 12 months previous to the improvements, and then predicting energy use for the 12 months immediately after the improvements using the same model. The threshold for verifiable energy savings resulting from building improvements was a maximum uncertainty of 50 percent at the 68 percent confidence level. Improvements involving original furnace or air conditioner replacement resulted in significant and verifiable reductions in energy use. Energy savings due to lighting improvements were verified for only one of seven buildings in which lighting was upgraded. Verifiable results were obtained in buildings with constant usage patterns, hours of operation, and equipment. Significant changes not related...