Accurate Energy Prediction Research Articles

NEW MODEL FOR SOLAR RADIATION ESTIMATION FROM MEASURED AIR TEMPERATURE AND RELATIVE HUMIDITY IN NIGERIA

Solar radiation prediction is essential for effective and reliable solar power project, predicted solar radiation can be used for accurate solar energy prediction. Solar radiation measurement is not sufficient in Nigeria for various reasons such as maintenance and repair cost, calibration of instrument, and expansive of measuring device. In this paper, adaptive neuro-fuzzy inference system (ANFIS) model was developed to predict the monthly average solar radiation in Nigeria. Air temperature of monthly mean minimum temperature, maximum temperature and relative humidity obtained from Nigerian Meteorological Agency (NIMET) were used as inputs to the ANFIS model and monthly mean global solar radiation was used as out of the model. Statistical evaluation of the model was done based on root mean square error (RMSE) and correlation coefficient R to examine the accuracy of the developed model. The values of RMSE and R for the training data are 0.91315MJ/m2 and 0.91264MJ/m2 respectively. The obtained result showed a good correlation between the predicted and measured solar radiation which proves ANFIS to be a good model for solar radiation prediction.  http://dx.doi.org/10.4314/njt.v36i3.35

Improvements in the predictions for the photovoltaic system performance of the Mediterranean regions

The performance assessment of photovoltaic (PV) systems is a complex process. Several meteorological data sources are available to evaluate the PV system generation. Different computing models can be applied to determinate the solar irradiance on the plane of the array (POA). The cooling effect of the PV module due to the wind speed should not be neglected. The present study may support several users to perform more accurate PV energy predictions, providing important suggestions to develop future PV system projects with more high reliability.Perez and Hay-Davies models for the computing of the irradiance on tilted surfaces are combined with three meteorological datasets, characterized by different monitoring period and meteo station location, to estimate the POA irradiance, the module temperature and PV energy output for a PV system located in the Mediterranean climate area. Prediction results are performed by the PVsyst tool and compared with the actual data. Simulations are carried out taking into account the wind effects on the PV module performance.Results demonstrate that the geographic features of the location, in which the weather station is located, have higher impact on the estimations of the PV system performance than the distance between the PV system and the meteo station. Perez and Hay-Davies models provide predictions of the PV energy and the module temperature with a difference up to 3% and 1% respectively. Yearly average wind speed in the range 2–4m/s fosters a cooling effect up to 3% higher than one due to the wind magnitude less of 2m/s, increasing the PV energy up to 1%.

An hourly hybrid multi-variate change-point inverse model using short-term monitored data for annual prediction of building energy performance, part I: Background (1404-RP)

The primary purpose of the research was to develop analysis methodologies by which the time period for field monitoring of energy use in buildings can be reduced to less than a whole year while satisfying preset accuracy levels of annual energy performance verification. Such methodologies would be of great benefit to the ongoing efforts to spur the diffusion of high-performance buildings by actual monitoring and to energy service companies and energy professionals who need a more cost-effective and acceptable alternative to year-long monitoring. The analysis has been completed in detail at two different time scales: daily and hourly. The research revealed that (1) 2 weeks of hourly monitored data can be adequate for producing accurate long-term whole building energy predictions, provided the monitoring is taken at a correct time, and (2) adding more data to the regression model (extending the length of the short-term monitoring period) can, contrary to intuition, make the predictive model much poorer. The analysis with both hourly and daily data revealed that the proper monitoring period is highly dependent on the time at which the data is taken, with the best time to collect data being during the swing seasons. To determine the amount of short-term building energy-use data that would be enough to generate acceptable annual predictions, four different daily analysis methods were tested on year-long data sets from three buildings, and five different hourly analysis methods were tested on year-long data sets from over 40 buildings. Several analysis methods were proposed and studied based on the type of data available. These categories include use of: (1) ambient temperature data only, (2) simulated/monitored data, (3) year-long utility bills and a short monitored dataset, and (4) year-long utility bills only. Three application areas for research of this nature were identified: detailed audits, green building performance verification, and verification of post-retrofit claims using pre–post monitored data. The results of the studies using the daily scales are already published (Singh et al. 2013, 2014). This paper is the first of three papers reporting on the hourly scale methodology and results, and covers the background of the research undertaken, providing a detailed literature survey that was necessary for conducting the work.

Piecewise linearity in theGWapproximation for accurate quasiparticle energy predictions

We identify the deviation from the straight line error (DSLE) -- i.e., the spurious non-linearity of the total energy as a function of fractional particle number -- as the main source for the discrepancy between experimental vertical ionization energies and theoretical quasiparticle energies, as obtained from the $GW$ and $GW$+SOSEX approximations to many-body perturbation theory (MBPT). For self-consistent calculations, we show that $GW$ suffers from a small DSLE. Conversely, for perturbative $G_0W_0$ and $G_0W_0$+SOSEX calculations the DSLE depends on the starting point. We exploit this starting-point dependence to reduce (or completely eliminate) the DSLE. We find that the agreement with experiment increases as the DSLE reduces. DSLE-minimized schemes, thus, emerge as promising avenues for future developments in MBPT.

Deep learning for estimating building energy consumption

To improve the design of the electricity infrastructure and the efficient deployment of distributed and renewable energy sources, a new paradigm for the energy supply chain is emerging, leading to the development of smart grids. There is a need to add intelligence at all levels in the grid, acting over various time horizons. Predicting the behavior of the energy system is crucial to mitigate potential uncertainties. An accurate energy prediction at the customer level will reflect directly in efficiency improvements in the whole system. However, prediction of building energy consumption is complex due to many influencing factors, such as climate, performance of thermal systems, and occupancy patterns. Therefore, current state-of-the-art methods are not able to confine the uncertainty at the building level due to the many fluctuations in influencing variables. As an evolution of artificial neural network (ANN)-based prediction methods, deep learning techniques are expected to increase the prediction accuracy by allowing higher levels of abstraction. In this paper, we investigate two newly developed stochastic models for time series prediction of energy consumption, namely Conditional Restricted Boltzmann Machine (CRBM) and Factored Conditional Restricted Boltzmann Machine (FCRBM). The assessment is made on a benchmark dataset consisting of almost four years of one minute resolution electric power consumption data collected from an individual residential customer. The results show that for the energy prediction problem solved here, FCRBM outperforms ANN, Support Vector Machine (SVM), Recurrent Neural Networks (RNN) and CRBM.

Unsupervised energy prediction in a Smart Grid context using reinforcement cross-building transfer learning

In a future Smart Grid context, increasing challenges in managing the stochastic local energy supply and demand are expected. This increased the need of more accurate energy prediction methods in order to support further complex decision-making processes. Although many methods aiming to predict the energy consumption exist, all these require labelled data, such as historical or simulated data. Still, such datasets are not always available under the emerging Smart Grid transition and complex people behaviour. Our approach goes beyond the state-of-the-art energy prediction methods in that it does not require labelled data. Firstly, two reinforcement learning algorithms are investigated in order to model the building energy consumption. Secondly, as a main theoretical contribution, a Deep Belief Network (DBN) is incorporated into each of these algorithms, making them suitable for continuous states. Thirdly, the proposed methods yield a cross-building transfer that can target new behaviour of existing buildings (due to changes in their structure or installations), as well as completely new types of buildings. The methods are developed in the MATLAB® environment and tested on a real database recorded over seven years, with hourly resolution. Experimental results demonstrate that the energy prediction accuracy in terms of RMSE has been significantly improved in 91.42% of the cases after using a DBN for automatically extracting high-level features from the unlabelled data, compared to the equivalent methods without the DBN pre-processing.

Occupant behaviour and robustness of building design

Occupant behaviour can cause major discrepancies between the designed and the real total energy use in buildings.A possible solution to reduce the differences between predictions and actual performances is designing robust buildings, i.e. buildings whose performances show little variations with alternating occupant behaviour patterns.The aim of this work was to investigate how alternating occupant behaviour patterns impact the performance of different envelope design solutions in terms of building robustness.Probabilistic models of occupants' window opening and use of shading were implemented in a dynamic building energy simulation tool (IDA ICE).The analysis was carried out by simulating 15 building envelope designs in different thermal zones of an Office Reference Building in 3 climates: Stockholm, Frankfurt and Athens.In general, robustness towards changes in occupants' behaviour increased with increasing thermal mass and with decreasing transparent area of the envelope.The importance of the robustness' evaluation is highlighted in this paper, in order to obtain optimized buildings' designs for more accurate and realistic energy predictions.

Performance and power profiling for emulated Android systems

Simulation is a common approach for assisting system design and optimization. For system-wide optimization, energy and computational resources are often the two most critical issues. Monitoring the energy state of each hardware component and measuring the time spent in each state is needed for accurate energy and performance prediction. For software optimization, it is important to profile the energy and the time consumed by each software construct in a realistic operating environment with a proper workload. However, the conventional approaches of simulation often fail to produce satisfying data. First, building a cycle-accurate simulation environment for a complex system, such as an Android smartphone, is difficult and can take a long time. Second, a slow simulation can significantly alter the behavior of multithreaded, I/O-intensive applications and can affect the accuracy of profiles. Third, existing software-based profilers generally do not work on simulators, which makes it difficult for performance analysis of complicated software, for example, Java applications executed by the Dalvik VM in an Android system. To address these aforementioned problems, we proposed and prototyped a framework, called virtual performance analyzer (VPA). VPA takes advantage of an existing emulator or virtual machine monitor to reduce the complexity of building a simulator. VPA allows the user to selectively and incrementally integrate timing models and power models into the emulator with our carefully designed performance/power monitors, tracing facility, and profiling tools to evaluate and analyze the emulated system. The emulated system can perform at different levels of speed to help verify if the profile data are impacted by the emulation speed. Finally, VPA supports existing software-based profiles and enables non-intrusive tracing/profiling by minimizing the probe effect. Our experimental results show that the VPA framework allows users to quickly establish a performance/power evaluation environment and gather useful information to support system design and software optimization for Android smartphones.

Energy Prediction Equations Are Inadequate for Obese Hispanic Youth

Assessing energy requirements is a fundamental activity in clinical dietetics practice. A study was designed to determine whether published linear regression equations were accurate for predicting resting energy expenditure (REE) in fasted Hispanic children with obesity (aged 7 to 15 years). REE was measured using indirect calorimetry; body composition was estimated with whole-body air displacement plethysmography. REE was predicted using four equations: Institute of Medicine for healthy-weight children (IOM-HW), IOM for overweight and obese children (IOM-OS), Harris-Benedict, and Schofield. Accuracy of the prediction was calculated as the absolute value of the difference between the measured and predicted REE divided by the measured REE, expressed as a percentage. Predicted values within 85% to 115% of measured were defined as accurate. Participants (n=58; 53% boys) were mean age 11.8±2.1 years, had 43.5%±5.1% body fat, and had a body mass index of 31.5±5.8 (98.6±1.1 body mass index percentile). Measured REE was 2,339±680 kcal/day; predicted REE was 1,815±401 kcal/day (IOM-HW), 1,794±311 kcal/day (IOM-OS), 1,151±300 kcal/day (Harris-Benedict), and, 1,771±316 kcal/day (Schofield). Measured REE adjusted for body weight averaged 32.0±8.4 kcal/kg/day (95% confidence interval 29.8 to 34.2). Published equations predicted REE within 15% accuracy for only 36% to 40% of 58 participants, except for Harris-Benedict, which did not achieve accuracy for any participant. The most frequently accurate values were obtained using IOM-HW, which predicted REE within 15% accuracy for 55% (17/31) of boys. Published equations did not accurately predict REE for youth in the study sample. Further studies are warranted to formulate accurate energy prediction equations for this population.

Variability in results from predicted resting energy needs as compared to measured resting energy expenditure in Korean children

Energy needs are influenced by many factors, including ethnicity. Multiple studies have shown that the accuracy of an energy prediction equation varies with the ethnic background of the study population. Therefore, it is crucial to identify the most accurate energy prediction equation to use for a given population. This study compared measured resting energy expenditure to results from commonly-used energy prediction equations to identify the most accurate equation to use for Korean children. Based on previous literature showing wide variation in accuracy of energy prediction equations in different ethnic groups, we hypothesized that results from measured- vs. predicted energy needs would be significantly different in this population. Subjects were 92 South Korean children (38 boys, 54 girls) age 7.7 +/- 2.7 years (mean +/- SD). Measurements included: resting metabolic rate (TrueOne 2400 metabolic cart), weight/height (digital scale/stadiometer); body fat (BIA, Inbody720), blood pressure (sphingomanometer), triceps skinfold thickness (MD-500 skinfold calipers), muscle mass (Heymsfield's formula) and body surface area (Dubois formula) calculations. Resting energy needs were predicted using the Harris-Benedict, WHO/NAO/FAO, Altman and Dittmer, Maffeis, and Schofield-HW equations, and the Dietary Reference Intake recommendations. Measured and predicted energy needs were significantly correlated (P < .001 for all; range R(2) = 0.54-0.56), yet significantly different for all equations studied (P < .05) except the Maffeis and Schofield-HW equations. Differences (means +/- SD) between measured vs. predicted energy needs ranged from 9.5 +/- 123.2 (Schofield-HW) to 199.6 +/- 132.7 (WHO/NAO/FAO) kcal/day, where a value closer to zero indicates increased accuracy of the prediction equation to correspond to measured energy needs. Although results from equations studied were significantly correlated with measured resting energy needs, notable discrepancies existed which, over time, could produce undesirable weight changes in Korean children.

Comprehensive DFT and MP2 Level Investigations of Reaction of 2,3-Dihydro-1,5-benzodiazepine-2-thiones with Hydrazine

Density functional theory approach was used for the 4-phenyl-2,3-dihydro-1,5-benzodiazepine-2-thione compound to determine the mechanism of hydrazinolysis of 4-substituted 2,3-dihydro-1,5-benzodiazepine-2-thiones. Single-point calculations at the MP2/6-311+G(d,p)//B3LYP/6-311+G(d,p) level were performed for the more accurate energy prediction. The solvent effect was taken into account by carrying out single-point calculations using the PCM methodology. The obtained results show that in the investigating mechanism the first step consists of the hydrazine molecule addition to the thiocarbonyl bond of the 4-phenyl-2,3-dihydro-1,5-benzodiazepine-2-thione following removal of H(2)S. Further addition of another hydrazine molecule to the azomethyne bond and cyclization with pyrazole ring formation occur, and then the diazepine ring-opening and the removal of hydrazine molecule proceed. Finally, imine-enamine tautomerization leads to 5-N-(2-aminophenyl-1-amino)-3-phenylpyrazole as a main product that is in agreement with the experimental observation. The cyclization step is a rate-determining step of this reaction.

Long-range-corrected hybrid density functionals including random phase approximation correlation: Application to noncovalent interactions

We recently presented a combination of a short-range density functional approximation with long-range random phase approximation (RPA) correlation [B. G. Janesko, T. M. Henderson, and G. E. Scuseria, J. Chem. Phys. 130, 081105 (2009)]. Here we show that this approach provides very accurate interaction energy predictions for a range of noncovalent complexes. Calculations on representative sets of hydrogen bonded, dipole-dipole, charge transfer, and weakly bound (van der Waals) complexes show that long-range RPA provides statistical errors comparable to CCSD(T) in moderate basis sets. This approach shows promise for providing accurate and computationally tractable models of noncovalent interactions in biological systems.

Lead Finder: An Approach To Improve Accuracy of Protein−Ligand Docking, Binding Energy Estimation, and Virtual Screening

An innovative molecular docking algorithm and three specialized high accuracy scoring functions are introduced in the Lead Finder docking software. Lead Finder's algorithm for ligand docking combines the classical genetic algorithm with various local optimization procedures and resourceful exploitation of the knowledge generated during docking process. Lead Finder's scoring functions are based on a molecular mechanics functional which explicitly accounts for different types of energy contributions scaled with empiric coefficients to produce three scoring functions tailored for (a) accurate binding energy predictions; (b) correct energy-ranking of docked ligand poses; and (c) correct rank-ordering of active and inactive compounds in virtual screening experiments. The predicted values of the free energy of protein-ligand binding were benchmarked against a set of experimentally measured binding energies for 330 diverse protein-ligand complexes yielding rmsd of 1.50 kcal/mol. The accuracy of ligand docking was assessed on a set of 407 structures, which included almost all published test sets of the following programs: FlexX, Glide SP, Glide XP, Gold, LigandFit, MolDock, and Surflex. rmsd of 2 A or less was observed for 80-96% of the structures in the test sets (80.0% on the Glide XP and FlexX test sets, 96.0% on the Surflex and MolDock test sets). The ability of Lead Finder to distinguish between active and inactive compounds during virtual screening experiments was benchmarked against 34 therapeutically relevant protein targets. Impressive enrichment factors were obtained for almost all of the targets with the average area under receiver operator curve being equal to 0.92.

Toward accurate relative energy predictions of the bioactive conformation of drugs

Quantifying the relative energy of a ligand in its target-bound state (i.e. the bioactive conformation) is essential to understand the process of molecular recognition, to optimize the potency of bioactive molecules and to increase the accuracy of structure-based drug design methods. This is, nevertheless, seriously hampered by two interrelated issues, namely the difficulty in carrying out an exhaustive sampling of the conformational space and the shortcomings of the energy functions, usually based on parametric methods of limited accuracy. Matters are further complicated by the experimental uncertainty on the atomic coordinates, which precludes a univocal definition of the bioactive conformation. In this article we investigate the relative energy of bioactive conformations introducing two major improvements over previous studies: the use sophisticated QM-based methods to take into account both the internal energy of the ligand and the solvation effect, and the application of physically meaningful constraints to refine the bioactive conformation. On a set of 99 drug-like molecules, we find that, contrary to previous observations, two thirds of bioactive conformations lie within 0.5 kcal mol(-1) of a local minimum, with penalties above 2.0 kcal mol(-1) being generally attributable to structural determination inaccuracies. The methodology herein described opens the door to obtain quantitative estimates of the energy of bioactive conformations and can be used both as an aid in refining crystallographic structures and as a tool in drug discovery.

Getting it right: modeling of pH, solvent and “nearly” everything else in virtual screening of biological targets

“Getting it right” refers to the careful modeling of all elements in the living system, i.e. biological macromolecules, ligands and water molecules. In addition, careful attention should be paid to the protonation state of ionizable functional groups on the ligands and residues at the active site. Computational technology based on the empirical HINT program is described to: (1) calculate free energy scores for ligand binding; (2) include the implicit and explicit effects of water in and around the ligand binding site; and (3) incorporate the effects of global and local pH in molecular models. This last point argues for the simultaneous consideration of a number of molecular models, each with different protonation profiles. Data from recent studies of protein–ligand systems (trypsin, thrombin, neuraminidase, HIV-1 protease and others) are used to illustrate the concepts in the paper. Also discussed are experimental factors related to accurate free energy predictions with this and other computational technologies.

Predicting energy needs in ventilator-dependent critically ill patients: effect of adjusting weight for edema or adiposity

Predicting energy needs in critical illness can be difficult because of uncertainties about the influence of multiple factors on energy expenditure. Understanding these components is important to avoid limiting optimal outcome by underfeeding and to avoid complications of overfeeding. Prediction strategies often use a patient's weight to estimate needs. For overweight patients, there is controversy as to whether actual or modified weight should be used in predictions. This study was designed to evaluate a proposed technique to improve the accuracy of predicting energy needs in critically ill, overweight subjects. Subjects' energy needs were predicted [with Harris-Benedict equation (HBE) and kilojoules per kilogram (KPK) strategies] by using both actual weight and an adjusted weight developed to attempt to more accurately reflect lean mass. Results were compared with measured energy expenditure determined by indirect calorimetry. Results indicated that use of actual weights in predictions for overweight subjects may lead to overfeeding. Use of adjusted weights led to more accurate energy predictions with the KPK than with the HBE strategy. Adjusted-weight strategies could explain > 45% of the variability of resting energy expenditure in subjects 130-159% of ideal body weight. Results of this study suggest that using adjusted weights with the KPK prediction strategy may be preferable for this population, particularly for patients > or = 130% of ideal body weight. This study also indicated that multiple diagnoses may not lead to increased energy requirements.

IMOMO(G2MS): A New High-Level G2-Like Method for Large Molecules and Its Applications to Diels−Alder Reactions

A new and less expensive G2-type approach, G2MS, which can be used for accurate energy prediction for up to seven to eight atoms has been proposed and tested against the standard G2 data set. The r...