J. Kelly Kissock

Targeting Residential Energy Assistance

This paper describes a four-step method to analyze the utility bills and weather data from multiple residences to target buildings for specific energy conservation retrofits. The method is also useful for focusing energy assessments on the most promising opportunities. The first step of the method is to create a three-parameter changepoint regression model of energy use versus weather for each building and fuel type. The three model parameters represent weather independent energy use, the building heating or cooling coefficient and the building balance-point temperature. The second step is to drive the models using typical TMY2 weather data to determine Normalized Annual Consumption (NAC) for each fuel type. The third step is to create a sliding NAC with each set of 12 sequential months of utility data. The final step is to benchmark the NACs and coefficients of multiple buildings to identify average, best and worst energy performers, and how the performance of each building has changed over time. The method identifies billing errors, normalizes energy use for changing weather, prioritizes sites for specific energy-efficiency retrofits and tracks weather-normalized changes in energy use. The principle differences between this method and previously defined ones are that this method seeks to use inverse modeling proactively to identify energy saving opportunities rather than retroactively to measure energy savings, it tracks changes in building performance using sliding analysis, and it uses comparisons between multiple buildings to extract additional information. This paper describes the method, then demonstrates the method through a case study of about 300 low-income residences. After applying the method, targeted buildings were visited to determine the accuracy of the method at identifying energy efficiency opportunities. The case study shows that over 80% of the targeted buildings presented at least one of the expected problems from each type of retrofit.

Industrial Assessment Center

The University of Dayton (UD) performed energy assessments, trained students and supported USDOE objectives. In particular, the UD Industrial Assessment Center (IAC) performed 96 industrial energy assessment days for mid-sized manufacturers. The average identified and implemented savings on each assessment were

Energy and waste reduction opportunities in industrial processes

261,080 per year and

Conceptual Design of Net Zero Energy Campus Residence

54,790 per year. The assessments served as direct training in industrial energy efficiency for 16 UD IAC students. The assessments also served as a mechanism for the UD IAC to understand manufacturing energy use and improve upon the science of manufacturing energy efficiency. Specific research results were published in 16 conference proceedings and journals, disseminated in 22 additional invited lectures, and shared with the industrial energy community through the UD IAC website.

Measuring Plant-Wide Energy Savings

ABSTRACT Traditional approach for reducing energy and waste in industrial processes typically focus on improving the efficiency of the primary energy conversion equipment. Unfortunately, this approach frequently results in incremental improvement at high costs, since most energy and mass conversion equipment is relatively efficient to begin with and upgrading to higher efficiency equipment is usually quite costly. In this article, we describe an alternative approach that begins by focusing outward to the distribution system and energy conversion equipment. We call this protocol the “INSIDE-OUT” approach, and suggest that it is a manifestation of the exergy analysis method. To support this assertion, we develop the thermodynamic bases for the “Out-side-in” and the “Inside-out” approaches to identifying savings We then demonstrate the comparative effectiveness of the “inside-out” approach using approaches from lighting, air compressors, and electroplating. Finally, we show why the inside-out approach leads ...

Energy Efficient Process Heating: Managing Air Flow

In response to both global and local challenges, the University of Dayton is committed to building a net-zero energy student residence, called the Eco-house. A unique aspect of the Eco-house is the degree of student involvement; in accordance with UD’s mission, interdisciplinary student teams from mechanical engineering, civil engineering and the humanities are leading the design effort. This paper discusses the conceptual design of a net-zero energy use campus residence, and the analysis completed thus far. Energy use of current student houses is analyzed to provide a baseline and to identify energy saving opportunities. The use of the whole-system inside-out approach to guide the overall design is described. Using the inside-out method as a guide, the energy impacts of occupant behavior, appliances and lights, building envelope, energy distribution systems and primary energy conversion equipment are discussed. The design of solar thermal and solar photovoltaic systems to meet the hot water and electricity requirements of the house is described. Eco-house energy use is simulated and compared to the energy use of the existing houses. The analysis shows the total source energy requirements of the Eco-house could be reduced by about 340 mmBtu per year over older baseline houses, resulting in CO2 emission reductions of about 54,000 lb per year and utility cost savings of about

Economic and Environmental Impacts of Energy Efficiency Investment on Local Manufacturers

3,000 per year. Detailed cost analysis and cost optimization have not been performed but are critical aspects of the UD Eco-house project, which will be performed in the future.Copyright

Improving Compressed Air Energy Efficiency in Automotive Plants - Practical Examples and Implementation

This paper presents a general method for measuring industrial energy savings and demonstrates the method using a case study from an actual industrial energy assessment. The method uses regression models to characterize baseline energy use. It takes into account changes in weather and production, and can use sub-metered data or whole plant utility billing data. In addition to calculating overall savings, the method is also able to disaggregate savings into components, which provides additional insight into the effectiveness of the individual savings measures. Although the method incorporates search techniques and multi-variable least-squares regression, it is easily implemented using data analysis software. The case study compared expected, unadjusted and weather-adjusted savings from six recommendations to reduce fuel use. The study demonstrates the importance of adjusting for weather variation between the preand post-retrofit periods. It also demonstrated the limitations of the engineering models when used to estimate savings. Introduction The decision to spend money to reduce energy expenditures frequently depends on the expected savings. Decision makers must then weigh the expected savings with several other issues. These issues include the availability of capital, competing investments, the synergy of the proposed retrofit with other strategic initiatives, and, not insignificantly, the certainty that the expected savings will be realized. This uncertainty about whether the expected savings will be realized depends largely on the type of retrofit. In some cases, it is relatively easy to verify expected savings; for example, expected energy savings from a lighting upgrade can be easily verified by measuring the power draw of lighting fixtures before and after a lighting upgrade. A history of verified savings reduces the uncertainty about future lighting recommendations and encourages this type of energy efficiency retrofit. In other cases, however, the retrofit may occur on a component of a larger system, and the energy use of the component may be difficult or impossible to meter. Moreover, the energy use may also be a function of weather and/or production, which frequently changes between the preand post retrofit periods. In these cases, it is more difficult to measure energy savings and, as a consequence, savings are seldom verified. This lack of verification hurts the effort to maximize industrial energy efficiency. In some cases, retrofit measures which would realize the expected savings are not implemented since there is no history of successful verification. In other cases, retrofits

Photovoltaic Module Assembly as Appropriate Technology in Pakistan

Much energy is lost through excess air flow in and out of process heating equipment. Energy saving opportunities from managing air flow include minimizing combustion air, preheating combustion air, minimizing ventilation air, and reconfiguring openings to reduce leakage. This paper identifies these opportunities and presents methods to quantify potential energy savings from implementing these energy-savings measures. Case study examples are used to demonstrate the methods and the potential energy savings. The method for calculating savings from minimizing combustion air accounts for improvement in efficiency from increased combustion temperature and decreased combustion gas mass flow rate. The method for calculating savings from preheating inlet combustion air consists of fundamental heat exchanger and combustion efficiency equations. This method accounts for the reduction of combustion air flow as fuel input declines, which is often neglected in many commonly-used methods. The method for calculating savings from reducing forced ventilation in ovens accounts for flow rate of ventilation air and air temperature when entering and exhausting the oven. The method for calculating savings from reconfiguring oven openings accounts for flow rate of air entering and exiting the oven due to buoyancy forces. INTRODUCTION Managing air flow is usually the most important aspect to consider when attempting to improve the energy efficiency of most process heating systems. The largest loss in fuelfired process heating is nearly always the loss through the exhaust stack, which is often greater than all other losses combined (Thekdi, 2005). For example, in a boiler, about 20% of input energy is lost in the exhaust gasses while only about 2% is lost through the boiler shell. For higher temperature applications, even more energy is lost in the exhaust gasses because they leave the system at higher temperatures. For example, boilers generating steam at 250 F to 350 F typically have efficiencies of about 80%. Furnaces that melt aluminum at 1,400 F have efficiencies of about 50%, and furnaces that melt glass at 2,500 F have efficiencies of about 30%. Although most or all air in a fuel-fired heating system leaves the system through the exhaust stack, air enters the system as combustion air, ventilation air, and infiltration air. Figure 1 shows a process heating system with the major categories of air flow.

A HYBRID METHOD FOR ESTIMATING NATURAL LIGHTING POTENTIAL IN BUILDINGS

The main goal of this study is to estimate the community-wide economic and environmental impacts of energy efficiency investment on the local manufacturing using data with different granularity. A systematic framework is developed by using multiple scale/layer of data. Result shows that a