00555nas a2200169 4500008003900000245004100039210004100080260003500121100002500156700002200181700002400203700002200227700002000249700002500269700003000294856006100324 1989 d00aThermal Energy Storage System Sizing0 aThermal Energy Storage System Sizing aVancouver, BC, Canadac01/19891 aDumortier, Dominique1 aKammerud, Ron, C.1 aBirdsall, Bruce, E.1 aAndersson, Brandt1 aEto, Joseph, H.1 aCarroll, William, L.1 aWinkelmann, Frederick, C. uhttp://www.ibpsa.org/proceedings/BS1989/BS89_357_362.pdf00368nas a2200109 4500008003900000245004400039210004000083260001200123100002000135700001800155856008500173 1988 d00aThe HVAC Costs of Fresh Air Ventilation0 aHVAC Costs of Fresh Air Ventilation c09/19881 aEto, Joseph, H.1 aMeyer, Cecile uhttps://simulationresearch.lbl.gov/publications/hvac-costs-fresh-air-ventilation00458nas a2200109 4500008003900000245008000039210006900119260003900188100002000227700001800247856008300265 1988 d00aThe HVAC Costs of Increased Fresh Air Ventilation Rates in Office Buildings0 aHVAC Costs of Increased Fresh Air Ventilation Rates in Office Bu aOttawa, ON, Canada.bLBNLc01/19881 aEto, Joseph, H.1 aMeyer, Cecile uhttps://simulationresearch.lbl.gov/publications/hvac-costs-increased-fresh-air00432nas a2200121 4500008003900000024001500039245004800054210004600102260003000148100002000178700002200198856009000220 1988 d aDA-88-21-100aModeling Cogeneration Systems with DOE-2.1C0 aModeling Cogeneration Systems with DOE21C aDallas, TXbLBNLc01/19881 aEto, Joseph, H.1 aGates, Steven, D. uhttps://simulationresearch.lbl.gov/publications/modeling-cogeneration-systems-doe-21c01083nas a2200133 4500008003900000245007600039210006900115260001800184490000700202520061200209100002000821700002600841856008200867 1988 d00aSaving Electricity in Commercial Buildings with Adjustable-Speed Drives0 aSaving Electricity in Commercial Buildings with AdjustableSpeed bIEEEc05/19880 v243 a
Fan and chiller energy savings achievable in commercial buildings with adjustable-speed drives are described. The savings are estimated with the aid of parametric simulations from a sophisticated, hourly building energy simulation model. Two prototypes-a single-zone retail store and a multizone medium office building-are simulated for five U.S. locations. The model incorporates part-load performance curves for both inlet vane and adjustable-speed drive controls for fans and centrifugal chillers. The results identify economic conditions that justify the added expense of adjustable-speed drives.
1 aEto, Joseph, H.1 aDe Almeida, Anibal, T uhttps://simulationresearch.lbl.gov/publications/saving-electricity-commercial01630nas a2200121 4500008003900000245010400039210006900143260001200212490000700224520117100231100002001402856008601422 1988 d00aOn Using Degree-days to Account for the Effects of Weather on Annual Energy Use in Office Buildings0 aUsing Degreedays to Account for the Effects of Weather on Annual c09/19880 v123 aTo better quantify the effects of conservation measures, degree.day-based techniques are commonly used to isolate weather.induced changes in building energy use. In this paper, we use a building energy simulation model, which allows us to hold fixed all influences on energy use besides weather, to evaluate several degree-day-based techniques. The evaluation is applied to simulated electricity and natural gas consumption for two large office building prototypes located in five U.8. climates. We review the development of degree day- based, weather-normalization techniques to identify issues for applying the techniques to office buildings and then evaluate the accuracy of the techniques with the simulated data. We conclude that, for the two office building prototypes and five U.8. locations examined, most techniques perform reasonably well; accuracy, in predicting annual consumption, is generally better than 10%. Our major finding is that accuracy among individual techniques is overwhelmed by circumstances outside the control of the analyst, namely, the choice of the initial year from which the normalization estimates are made.
1 aEto, Joseph, H. uhttps://simulationresearch.lbl.gov/publications/using-degree-days-account-effects00440nas a2200097 4500008003900000245008800039210006900127260004100196100002000237856008500257 1985 d00aA Comparison of Weather Normalization Techniques for Commercial Building Energy Use0 aComparison of Weather Normalization Techniques for Commercial Bu aClearwater Beach, FL bLBNLc12/19851 aEto, Joseph, H. uhttps://simulationresearch.lbl.gov/publications/comparison-weather-normalization00440nas a2200097 4500008003900000245009000039210006900129260003600198100002000234856008800254 1985 d00aCooling Strategies Based on Indicators of Thermal Storage in Commercial Building Mass0 aCooling Strategies Based on Indicators of Thermal Storage in Com aCollege Station, Texasc09/19851 aEto, Joseph, H. uhttps://simulationresearch.lbl.gov/publications/cooling-strategies-based-indicators01406nas a2200193 4500008003900000024001800039245010700057210006900164260002400233520072900257653002500986653001901011653002401030653001601054653001701070100002001087700001601107856008901123 1985 d aEEB-BED-85-0500aImplications of Office Building Thermal Mass and Multi-day Temperature Profiles for Cooling Strategies0 aImplications of Office Building Thermal Mass and Multiday Temper aDenver, COc08/19853 aThis paper describes a study of the cooling energy requirements that result from thermal storage in building mass, and suggests methods for predicting and controlling its energy cost implications. The study relies on computer simulations of energy use for a large office building prototype in El Paso, TX using the DOE-2 building energy analysis program. Increased Monday cooling energy requirements resulting from the weekend shut-down of HVAC systems are documented. Predictors of energy use and peak demands, which account for thermal storage in building mass, are described. Load-shifting, sub-cooling and pre-cooling equipment operating strategies are evaluated with explicit reference to utility rate schedules.
10acommercial buildings10acooling energy10aenergy conservation10apeak demand10athermal mass1 aEto, Joseph, H.1 aPowell, Gay uhttps://simulationresearch.lbl.gov/publications/implications-office-building-thermal00539nas a2200145 4500008003900000245005700039210005500096260007100151100002100222700001700243700002000260700002200280700003000302856006100332 1985 d00aNew Features of the DOE-2.1c Energy Analysis Program0 aNew Features of the DOE21c Energy Analysis Program bInternational Building Performance Simulation Associationc01/19851 aBuhl, Walter, F.1 aErdem, Ender1 aEto, Joseph, H.1 aHirsch, James, J.1 aWinkelmann, Frederick, C. uhttp://www.ibpsa.org/proceedings/BS1985/BS85_195_200.pdf00378nas a2200097 4500008003900000245005200039210005200091260002800143100002000171856008900191 1984 d00aCommercial Building Cogeneration Opportuntities0 aCommercial Building Cogeneration Opportuntities aSanta Cruz, CAc08/19841 aEto, Joseph, H. uhttp://aceee.org/files/proceedings/1984/data/papers/SS84_Panel1_Paper_059.pdf#page=100595nas a2200181 4500008003900000245004700039210004200086260002300128100002400151700002400175700002100199700001700220700002000237700002200257700002100279700003000300856008300330 1984 d00aThe DOE-2 Building Energy Analysis Program0 aDOE2 Building Energy Analysis Program aSingaporec05/19841 aCurtis, Richard, B.1 aBirdsall, Bruce, E.1 aBuhl, Walter, F.1 aErdem, Ender1 aEto, Joseph, H.1 aHirsch, James, J.1 aOlson, Karen, H.1 aWinkelmann, Frederick, C. uhttps://simulationresearch.lbl.gov/publications/doe-2-building-energy-analysis00395nas a2200097 4500008003900000245006400039210006300103260002500166100002000191856008600211 1983 d00aOptimal Cogeneration Systems for High-Rise Office Buildings0 aOptimal Cogeneration Systems for HighRise Office Buildings aOrlando, FLc05/19831 aEto, Joseph, H. uhttps://simulationresearch.lbl.gov/publications/optimal-cogeneration-systems-high