Abstract: The overall thermal performance of a building UATotal can be empirically estimated through a short-duration controlled test. Preferably, the controlled test is performed at night during the winter. A heating source is turned off after the indoor temperature has stabilized. After an extended period, such as 12 hours, the heating source is briefly turned back on, such as for an hour, then turned off. The indoor temperature is allowed to stabilize. The energy consumed within the building during the test period is assumed to equal internal heat gains. Overall thermal performance is estimated by balancing the heat gained with the heat lost during the test period.

Abstract: The calculation of the variance of a correlation coefficient matrix for a photovoltaic fleet can be completed in linear space as a function of decreasing distance between pairs of photovoltaic plant locations. When obtaining irradiance data from a satellite imagery source, irradiance statistics must first be converted from irradiance statistics for an area into irradiance statistics for an average point within a pixel in the satellite imagery. The average point statistics are then averaged across all satellite pixels to determine the average across the whole photovoltaic fleet region. Where pairs of photovoltaic systems are located too far away from each other to be statistically correlated, the correlation coefficients in the matrix for that pair of photovoltaic systems are effectively zero. Consequently, the double summation portion of the calculation can be simplified to eliminate zero values based on distance between photovoltaic plant locations, substantially decreasing the size of the problem space.

Abstract: Gross energy load can be determined by combining periodic net load statistics, such as provided by a power utility or energy agency, with on-site power generation, such as photovoltaic power generation, as produced over the same time period. The gross energy load provides an indication upon which other types of energy investment choices can be evaluated. These choices can include traditional energy efficiencies, such as implementing electrical efficiency measures, which includes cutting down on and avoiding wasteful energy use and switching to energy efficient fixtures, and improving the thermal efficiency and performance of a building. The choices can also include non-traditional energy efficiencies, such as replacing a gasoline-powered vehicle with an electric vehicle, fuel switching from a water heater fueled by natural gas to a heat pump water heater, and fuel switching from space heating fueled by natural gas to a heat pump space heater.

Abstract: Potential energy investment scenarios can be evaluated. Energy performance specifications and prices for both existing and proposed energy-related equipment are selected, from which an initial capital cost is determined. The equipment selections are combined with current fuel consumption data, thermal characteristics of the building, and, as applicable, solar resource and other weather data to create an estimate of the fuel consumption of the proposed equipment. An electricity bill is calculated for the proposed equipment, from which an annual cost is determined. The payback of the proposed energy investment is found by comparing the initial and annual costs.

Abstract: The overall thermal performance of a building UATotal can be empirically estimated through a short-duration controlled test. Preferably, the controlled test is performed at night during the winter. A heating source, such as a furnace, is turned off after the indoor temperature has stabilized. After an extended period, such as 12 hours, the heating source is briefly turned back on, such as for an hour, then turned off. The indoor temperature is allowed to stabilize. The energy consumed within the building during the test period is assumed to equal internal heat gains. Overall thermal performance is estimated by balancing the heat gained with the heat lost during the test period.

Abstract: The accuracy of photovoltaic simulation modeling is predicated upon the selection of a type of solar resource data appropriate to the form of simulation desired. Photovoltaic power simulation requires irradiance data. Photovoltaic energy simulation requires normalized irradiation data. Normalized irradiation is not always available, such as in photovoltaic plant installations where only point measurements of irradiance are sporadically collected or even entirely absent. Normalized irradiation can be estimated through several methodologies, including assuming that normalized irradiation simply equals irradiance, directly estimating normalized irradiation, applying linear interpolation to irradiance, applying linear interpolation to clearness index values, and empirically deriving irradiance weights. The normalized irradiation can then be used to forecast photovoltaic fleet energy production.

Abstract: A system and method to evaluate building heating fuel consumption with the aid of a digital computer is described. The evaluation can be used for quantifying personalized electric and fuel bill savings. Such savings may be associated with investment decisions relating to building envelope improvements; HVAC equipment improvements; delivery system efficiency improvements; and fuel switching. The results can also be used for assessing the cost/benefit of behavioral changes, such as changing thermostat temperature settings. Similarly, the results can be used for optimizing an HVAC control system algorithm based on current and forecasted outdoor temperature and on current and forecasted solar irradiance to satisfy consumer preferences in a least cost manner. Finally, the results can be used to correctly size a photovoltaic (PV) system to satisfy needs prior to investments by anticipating existing energy usage and the associated change in usage based on planned investments.

Abstract: A system and method for net load-based inference of operational specifications of a photovoltaic power generation system with the aid of a digital computer are provided. Photovoltaic plant configuration specifications can be accurately inferred with net load data and measured solar resource data. Power generation data is simulated for a range of hypothetical photovoltaic system configurations based on a normalized solar power simulation model. Net load data is estimated based on one or more component loads. The set of key parameters corresponding to the net load estimate that minimizes total squared error represents the inferred specifications of the photovoltaic plant configuration.

Abstract: The calculation of the variance of a correlation coefficient matrix for a photovoltaic fleet can be completed in linear space as a function of decreasing distance between pairs of photovoltaic plant locations. When obtaining irradiance data from a satellite imagery source, irradiance statistics must first be converted from irradiance statistics for an area into irradiance statistics for an average point within a pixel in the satellite imagery. The average point statistics are then averaged across all satellite pixels to determine the average across the whole photovoltaic fleet region. Where pairs of photovoltaic systems are located too far away from each other to be statistically correlated, the correlation coefficients in the matrix for that pair of photovoltaic systems are effectively zero. Consequently, the double summation portion of the calculation can be simplified to eliminate zero values based on distance between photovoltaic plant locations, substantially decreasing the size of the problem space.

Abstract: Long-term photovoltaic system degradation can be predicted through a simple, low-cost solution. The approach requires the configuration specification for a photovoltaic system, as well as measured photovoltaic production data and solar irradiance, such as measured by a reliable third party source using satellite imagery. Note the configuration specification can be derived. This information is used to simulate photovoltaic power production by the photovoltaic system, which is then evaluated against the measured photovoltaic production data to determine the degree of error between simulated and measured production. The simulated production is adjusted to account for the error and to infer degradation that can be projected over time to forecast long-term photovoltaic system degradation.

Abstract: A system and method for modeling building thermal performance parameters through empirical testing with the aid of a digital computer is described. Three building-specific parameters, thermal conductivity, thermal mass, and effective window area, are empirically derived. Thermal conductivity is evaluated through an empirical test conducted in the absence of solar gain with constant indoor temperature and no HVAC. Thermal mass is evaluated through a second empirical test conducted in the absence of solar gain and no HVAC. Effective window area is evaluated through a third empirical test conducted in the presence of solar gain and no HVAC. Thermal HVAC system power rating and conversion and delivery efficiency are also parametrized. The parameters are estimated using short duration tests that last at most several days. A value of energy savings associated with changing one or more parameters is evaluated.

Abstract: A system for tuning a photovoltaic power generation plant forecast with the aid of a digital computer is provided. Global horizontal irradiance (GHI), ambient temperature and wind speed for a photovoltaic power generation plant over a forecast period are obtained. Simulated plane-of-array (POA) irradiance is generated from the GHI and the plant's photovoltaic array configuration as a series of simulated observations. Inaccuracies in GHI conversion are identified and the simulated POA irradiance at each simulated observation is corrected based on the conversion inaccuracies. Simulated module temperature is generated based on the simulated POA irradiance, ambient temperature and wind speed. Simulated power generation over the forecast period is generated based on the simulated POA irradiance, simulated module temperature and the plant's specifications and status.

Abstract: A system and method to evaluate building heating fuel consumption with the aid of a digital computer is described. The evaluation can be used for quantifying personalized electric and fuel bill savings. Such savings may be associated with investment decisions relating to building envelope improvements; HVAC equipment improvements; delivery system efficiency improvements; and fuel switching. The results can also be used for assessing the cost/benefit of behavioral changes, such as changing thermostat temperature settings. Similarly, the results can be used for optimizing an HVAC control system algorithm based on current and forecasted outdoor temperature and on current and forecasted solar irradiance to satisfy consumer preferences in a least cost manner. Finally, the results can be used to correctly size a photovoltaic (PV) system to satisfy needs prior to investments by anticipating existing energy usage and the associated change in usage based on planned investments.

Abstract: A system and method for forecasting fuel consumption for indoor thermal conditioning using thermal performance forecast approach with the aid of a digital computer are provided. Average daily outdoor temperatures for a time period are obtained. Historical daily fuel consumption for the time period is obtained. The historical daily fuel consumption is converted into an average daily fuel usage rates for the time period. A continuous frequency distribution of occurrences of the average daily outdoor temperatures is generated. A plot of the daily fuel usage rates versus the average daily outdoor temperatures is created. Fuel consumption for at least a portion of the time period is calculated based on sampling the daily fuel usage rate along a range of average daily outdoor temperatures times the temperatures' respective frequencies of occurrence.

Abstract: A computer-implemented system and method to assist consumers with decisions affecting a change in fuel requirements is provided. Fuel consumption for heating can be considered by evaluating changes that would affect thermal conductivity, average indoor temperature, HVAC efficiency, and solar gain. In a further embodiment, a computer-implemented system and method to evaluate investment's in a building's shell is provided. Thermal conductivity and the surface area of a surface that is under consideration for improvement are obtained, after which revised thermal conductivity can be modeled based on the existing and proposed thermal performance of that building surface. In a still further embodiment, fuel consumption for heating modeling results can be comparatively evaluated, with one fuel consumption model operating over an annual (or periodic) scope and another fuel consumption model operating on an hourly (or interval) scope.

Abstract: A computer-implemented system and method to determine building thermal performance parameters through empirical testing is described. Three building-specific parameters, thermal conductivity, thermal mass, and effective window area, are empirically derived. Thermal conductivity is evaluated through an empirical test conducted in the absence of solar gain with constant indoor temperature and no HVAC. Thermal mass is evaluated through a second empirical test conducted in the absence of solar gain and no HVAC. Effective window area is evaluated through a third empirical test conducted in the presence of solar gain and no HVAC. Thermal HVAC system power rating and conversion and delivery efficiency are also parametrized. The parameters are estimated using short duration tests that last at most several days. The parameters and estimated HVAC system efficiency can be used to simulate a time series of indoor building temperature, annual fuel consumption, or maximum indoor temperature.

Abstract: A system and method to evaluate building heating fuel consumption with the aid of a digital computer is described. The evaluation can be used for quantifying personalized electric and fuel bill savings. Such savings may be associated with investment decisions relating to building envelope improvements; HVAC equipment improvements; delivery system efficiency improvements; and fuel switching. The results can also be used for assessing the cost/benefit of behavioral changes, such as changing thermostat temperature settings. Similarly, the results can be used for optimizing an HVAC control system algorithm based on current and forecasted outdoor temperature and on current and forecasted solar irradiance to satisfy consumer preferences in a least cost manner. Finally, the results can be used to correctly size a photovoltaic (PV) system to satisfy needs prior to investments by anticipating existing energy usage and the associated change in usage based on planned investments.

Abstract: A system and method to determine building thermal performance parameters through empirical testing is described. The parameters can be formulaically applied to determine fuel consumption and indoor temperatures. To generalize the approach, the term used to represent furnace rating is replaced with HVAC system rating. As total heat change is based on the building's thermal mass, heat change is relabeled as thermal mass gain (or loss). This change creates a heat balance equation that is composed of heat gain (loss) from six sources, three of which contribute to heat gain only. No modifications are required for apply the empirical tests to summer since an attic's thermal conductivity cancels out and the attic's effective window area is directly combined with the existing effective window area. Since these tests are empirically based, the tests already account for the additional heat gain associated with the elevated attic temperature and other surface temperatures.

Abstract: Statistically representing point-to-point photovoltaic power estimation and area-to-point conversion of satellite pixel irradiance data are described. Accuracy on correlated overhead sky clearness is bounded by evaluating a mean and standard deviation between recorded irradiance measures and the forecast irradiance measures. Sky clearness over the two locations is related with a correlation coefficient by solving an empirically-derived exponential function of the temporal distance. Each forecast clearness index is weighted by the correlation coefficient to form an output set of forecast clearness indexes and the mean and standard deviation are proportioned. Additionally, accuracy on correlated satellite imagery is bounded by converting collective irradiance into point clearness indexes. A mean and standard deviation for the point clearness indexes is evaluated. The mean is set as an area clearness index for the bounded area.

Abstract: The accuracy of photovoltaic simulation modeling is predicated upon the selection of a type of solar resource data appropriate to the form of simulation desired. Photovoltaic power simulation requires irradiance data. Photovoltaic energy simulation requires normalized irradiation data. Normalized irradiation is not always available, such as in photovoltaic plant installations where only point measurements of irradiance are sporadically collected or even entirely absent. Normalized irradiation can be estimated through several methodologies, including assuming that normalized irradiation simply equals irradiance, directly estimating normalized irradiation, applying linear interpolation to irradiance, applying linear interpolation to clearness index values, and empirically deriving irradiance weights. The normalized irradiation can then be used to forecast photovoltaic fleet energy production.