Energy savings based upon use of smart WiFi thermostats ranging from 10 to 15% have been documented, as new features such as geofencing have been added. Here, a new benefit of smart WiFi thermostats is identified and investigated; namely, as a tool to improve the estimation accuracy of residential energy consumption and, as a result, estimation of energy savings from energy system upgrades, when only monthly energy consumption is metered. This is made possible from the higher sampling frequency of smart WiFi thermostats. In this study, collected smart WiFi data are combined with outdoor temperature data and known residential geometrical and energy characteristics. Most importantly, unique power spectra are developed for over 100 individual residences from the measured thermostat indoor temperature in each and used as a predictor in the training of a singular machine learning models to predict consumption in any residence. The best model yielded a percentage mean absolute error (MAE) for monthly gas consumption ±8.6%. Applied to two residences to which attic insulation was added, the resolvable energy savings percentage is shown to be approximately 5% for any residence, representing an improvement in the ASHRAE recommended approach for estimating savings from whole-building energy consumption that is deemed incapable at best of resolving savings less than 10% of total consumption. The approach posited thus offers value to utility-wide energy savings measurement and verification.

Voice in IP networks is transmitted as packets over IP (VoIP), the voice signals are converted to IP packets after beingdigitized and compressed for transmission. However, some packets can be missed in their way to the receiving side, due to network congestion. The loss of these packets degrades the speech quality in the listener side at VoIP system transmission. Since voice is transmitted is real time, the receiver cannot request a retransmission for any lost packets. Voice and data multiplexing in VoIP network always face problems when huge TCP traffic is transmitted resulting the voice packet to be stuck in the network during congestion. Therefore, VoIP packets will be delayed. Since delay and loss are the main parameters that affect the quality of a voice signal in a VoIP network.

This paper presents a design and a simulation study of voice and data integration in a VoIP network and analyses which scenario will suite the best performance for voice packets in traffic congestion to have a high voice quality rating when using a single data TCP source and a multiple TCP sources when multiplexed with a UDP voice source. This is accomplished by using NS-2 network simulator (version 2), and M/D/1 queue type with various queuing systems such as First in First out (FIFO), Fair queue (FQ) and Deficit Round Robin (DRR), which represent the technique mechanism to serve voice and data packets in a queueing system. Then loss and delay are measured for each scenario to determine the quality of voice.

Predicting cooling load is essential for many applications such as diagnosing the health of existing chillers, providing better control functionality, and minimizing peak loads. In this study, short-term chiller and total building demand are acquired for five different commercial buildings in the Midwest USA. Four different machine learning models are then used to predict the chiller demand using the total building demand, outdoor weather data, and day/time information. Two data collection scenarios are considered. The first relies upon use of multiple weeks of data collection that includes very warm periods and season transitional periods where the outdoor temperature ranged from very warm to cool conditions in order to envelope all cooling season weather conditions. The second scenario employs use of contiguous data for a several weeks during only the warmest period of the year. The results show that using two or more separate time periods to envelope most of the weather data yields a much more accurate model in comparison to use of data for only one time period. These research findings have importance to energy service companies which often do short term audits (measurements) in order to estimate potential savings from chiller system upgrades (controls or otherwise).

In certain extremely low probability, severe accident scenarios which have been postulated for liquid metal cooled fast reactors,large bubble cavities containing fuel vapor and fission products transit a layer of coolant and release this material to the cover gas thereby presenting a contribution to an accident-specific source term [5].Mie model in radiation heat transfer has been investigated to analysis and interpret the experiments that conducted during 1980's for oxide UO 2 fueled reactors in Fuel Aerosol Simulant Test (FAST) facility at Oak Ridge National Laboratory (ORNL).These analyses are applied to estimate the bubble collapse of Liquid Metal reactors (LMR's) during a hypothetical core disruptive accident (HCDA).InMie scattering model the particle size was 0.07 µm [6]. The scattering coefficient of UO 2 particles (σ = 1.24 m-1), was calculated by using Mie theory,at the same number of stable nuclei's N (2.9 E15 nuclei/m 3) that resulted from theabsorbed coefficientk = 0.082 m-1 [7].P 1 approximation method was used to solve the radiative heat transfer equation (RTE) in spherical coordinates of participating medium confined between the two concentric spheres.The surfaces of the spheres are assumed to be gray, diffusely emitting and diffusely reflecting boundaries, and an isothermal boundary conditions were assumed at these surfaces.Marsak's boundary condition was to computed, the net radiative heat flux q(τ), and the incident radiation G(τ), to analyze and interpret the CVD experiments data that were conducted in the FAST facility at ORNL [8] and Fast Flux Test Facility reactor (FFTF) in Argonne National Laboratory ANL.The conclude that extracted from this study is greater margin of safety when the bubble rising time is greater than the bubble collapse time since the bubble collapses (UO 2 condenses) before it can reach the top of the vessel therefore there is less chance of release of aerosol as in Oak Ridge National Laboratory (ORNL) FAST experiments and Argonne National Laboratory (FFTF) reactor.

In certain extremely low probability, severe accident scenarios which have been postulated for liquid metal cooled fast reactors, large bubble cavities containing fuel vapor and fission products transit a layer of coolant and release this material to the cover gas thereby presenting a contribution to an accident-specific source term [5]. Rayleigh model in radiation heat transfer has been investigated to analysis and interpret the experiments that conducted during 1980's for oxide UO 2 fueled reactors in Fuel Aerosol Simulant Test (FAST) facility at Oak Ridge National Laboratory (ORNL).These analyses are applied to estimate the bubble collapse of Liquid Metal reactors (LMR's) during a hypothetical core disruptive accident (HCDA). In Rayleigh non-scattering model the particle size was 0.01 µm [6],and according to Mie theory principle, the absorption coefficient for small particle-size distribution was estimated (k = 10 m-1 was used) from reference [7] at complex refractive index of UO 2 at λ = 600 µm and x = 0.0785.A MATLAB code was used to solvethe radiative heat equation (RTE) in spherical coordinates. The mixture is in local thermodynamic equilibrium inside the bubble which has a black body surface boundary.The mixture in the cavity contains three components: the non-condensable gas Xenon, Uranium dioxide vapor, and fog.To simulate fuel bubble's geometry as realistically as possible, according to experimental observation, the energy equation in a spherical coordinate system has been solved with the radiative flux heat transfer equation (RTE) to obtain the effect of fuel bubble's geometry on the transient radiative heat flux and to predict the transient temperature distribution in the participating medium during a hypothetical core disruptive accident (HCDA) for liquid metal fast breeding reactor (LMFBR) for FAST. The transient temperature distribution in fog region was utilized to predict the amount of condensable UO 2 vapor = − ! " ! #. The conclusion that can be drawn from the present study, is that the Fuel Aerosol Simulant Test (FAST) facility at Oak Ridge National Laboratory has a larger margin of safety since the bubble rising time is greater than the bubble collapse time.

Abstract: A conventional ground-coupled heat pump (GCHP) can be used to supplement heat

rejection or extraction, creating a hybrid system that is cost-eective for certainly unbalanced climes.

This research explores the possibility for a hybrid GCHP to use excess heat from a combined heat

power (CHP) unit of natural gas in a heating-dominated environment for smart cities. A design for

a multi-family residential building is considered, with a CHP sized to meet the average electrical

load of the building. The constant electric output of the CHP is used directly, stored for later use in a

battery, or sold back to the grid. Part of the thermal output provides the building with hot water,

and the rest is channeled into the GCHP borehole array to support the building’s large heating needs.

Consumption and weather data are used to predict hourly loads over a year for a specific multi-family

residence. Simulations of the energies exchanged between system components are performed, and a

cost model is minimized over CHP size, battery storage capacity, number of boreholes, and depth of

the borehole. Results indicate a greater cost advantage for the design in a severely heated (Canada)

climate than in a moderately imbalanced (Ohio) climate.

The multi-family residential building sector is the least energy efficient in the United States, thus allowing for ample opportunities for significant cost-effective energy and carbon savings. In the present study, we propose a district solar borehole thermal solar energy storage (BTES) system for both retrofit and new construction for a multi-family residence in the Midwestern United States, where the climate is moderately cold with very warm summers. Actual apartment interval power and water demand data was mined and used to estimate unit level hourly space and water heating demands, which was subsequently used to design a cost-optimal BTES system. Using a dynamic simulation model to predict the system performance over a 25-year period, a parametric study was conducted that varied the sizes of the BTES system and the solar collector array. A life-cycle cost analysis concluded that is it possible for an optimally-sized system to achieve an internal rate of return (IRR) of 11%, while reducing apartment-wide energy and carbon consumption by 46%. Both a stand-alone and solar-assisted ground-source heat pump system were designed and simulated for comparison to the BTES system, and found to be less economically favorable than the solar BTES system. Thus, the promise for district-scale adoption of BTES in multi-family residences is established, particularly for new buildings.

A novel geothermal heat pump (GHP) system with an integrated low- to moderate-temperature salt hydrate phase change material (PCM) storage tank for buildings in cold climates is proposed in this study. The purpose of the PCM storage tank is to dampen peak heating loads and to remove annual ground thermal load imbalances on the ground heat exchanger (GHX) to assist in achieving an optimally-sized GHX. As heat is extracted from the closed-loop system by heat pumps in heating mode, a significant portion of this heat is used to solidify a salt hydrate PCM. This heat of fusion is later released back into the heat transfer fluid, storing it in the PCM tank and GHX for later diurnal and seasonal use. To examine the merits of the proposed concept, electric utility meter data on 15-minute time intervals were mined from an actual apartment building and used to estimate space heating, cooling, and hot water heating loads. Those data were used in an hourly, dynamic 20-year life-cycle simulation model in TRNSYS to design an optimum combination of GHX and PCM storage, where each component was sized to balance the annual ground thermal loads. The system simulation results show significant potential for GHX size reduction with a PCM storage tank, but the system is quite sensitive to the PCM melt temperature due to significant hysteretic nature of the salt hydrate PCM heating and cooling curves. We also find that there is no unique optimum unless other factors are considered such as installation cost and physical constraints; many combinations of GHX size and PCM mass are capable of achieving the design goal with similar annual electric energy consumption. For the cases examined here, a PCM melt temperature of 27 °C yields the most favorable economic results, and a preliminary economic analysis suggests that with typical drilling cost and PCM tank cost values, the GHX size can be reduced by over 50 %.