No description available.

No description available.

Because of shortage of natural resources, Taiwan must rely on the imported fossil fuels such as coal and petroleum for power generation. The demand of fossil fuel for all over the world causes increasing energy cost and global warming. Thus, to execute energy-saving policies and to reduce the amount of carbon producing can help many countries to decrease the amount of energy usage and global warming. This thesis proposes a intelligent digital meter by integrating a energy metering IC, microprocessor, and RS485 which support demand response to control loads for residential and commercial customers. The master station can execute real-time management to measure different power consumption, by each load device to support the analysis of customer consumption, and load forecasting with RS485. From the result, the developed intelligent digital meter to verify residential or commercial energy-saving effect and potential.

Due to lack of natural resources, a lot of fossil fuels such as coal and petroleum has to be imported for power generation. Both energy demand all over the world and the price of energy have been increased. To slow down the speed of global warming and to reduce the amount of carbon producing, various energy-saving policies have been applied by many counties to reduce the amount of energy usage. This thesis proposes a energy management system by integrating a ZigBee-based remote controller and a multi-function digital meter for residential and commercial customers. It can be easily installed without requesting modification of original circuits of electric appliances to achieve load for the demand response of the power company. The master station reads the power consumption of appliance through the RS-232 interface and detects controls the state of the remote device. With VB software, the system can measure the amount of power usage, by each load device to support the analysis of customer consumption. The load reduction can easily be achieved with the remote control according to the demand response issued by utility control center. By measuring and verifying the energy management system, the performance of power management is improved.

Chemnitz, Techn. Universiẗat, Diss., 2002.

Indiana University-Purdue University Indianapolis (IUPUI) / Electric demand charge is a large portion (usually 40%) of electric bill in residential, commercial, and manufacturing sectors. This charge is based on the greatest of all demands that have occurred during a month recorded by utility provider for an end-user. During the past several years, electric demand forecasting have been broadly studied by utilities on account of the fact that it has a crucial impact on planning resources to provide consumers reliable power at all time; on the other hand, not many studies have been conducted on consumer side. In this thesis, a novel Maximum Daily Demand (MDD) forecasting method, called Adaptive-Rate-of-Change (ARC), is proposed by analysing real-time demand trend data and incorporating moving average calculations as well as rate of change formularization to develop a forecasting tool which can be applied on either utility or consumer sides. ARC algorithm is implemented on two different real case studies to develop very short-term load forecasting (VSTLF), short-term load forecasting (STLF), and medium-term load forecasting (MTLF). The Chi-square test is used to validate the forecasting results. The results of the test reveal that the ARC algorithm is 84% successful in forecasting maximum daily demands in a period of 72 days with the P-value equals to 0.0301. Demand charge is also estimated to be saved by $8,056 (345.6 kW) for the first year for case study I (a die casting company) by using ARC algorithm. Following that, a new Maximum Demand Management (MDM) method is proposed to provide electric consumers a complete package. The proposed MDM method broadens the electric consumer understanding of how MDD is sensitive to the temperature, production, occupancy, and different sub-systems. The MDM method are applied on two different real case studies to calculate sensitivities by using linear regression models. In all linear regression models, R-squareds calculated as 0.9037, 0.8987, and 0.8197 which indicate very good fits between fitted values and observed values. The results of proposed demand forecasting and management methods can be very helpful and beneficial in decision making for demand management and demand response program.

No description available.

This study illustrates the use of ACNielsen Homescan Panel (HSD) in three separate demand analyses of dairy products: (1) the effect of using cross-sectional data in a New Empirical Industrial Organization (NEIO) study of ice cream firm mergers in San Antonio; (2) the estimation of hedonic price models for fluid milk by quart, halfgallon and gallon container sizes; (3) the estimation of a demand system including white milk, flavored milk, carbonated soft drinks, bottled water, and fruit juice by various container sizes. In the NEIO study a standard LA/AIDS demand system was used to estimate elasticities evaluating seven simulated mergers of ice cream manufactures in San Antonio in 1999. Unlike previously published NEIO work, it is the first to use crosssectional data to address the issue associated with inventory effects. Using the method developed by Capps, Church and Love, none of the simulated price effects associated with the mergers was statistically different from zero at the 5% confidence level. In 1995 Nerlove proposed a quantity-dependent hedonic model as a viable alternative to the conventional price-dependent hedonic model as a means to ascertain consumer willingness to pay for the characteristics of a given good. We revisited Nerlove’s work validating his model using transactional data indigenous to the HSD. Hedonic models, both price-dependent and quantity-dependent, were estimated for the characteristics of fat content, container type, and brand designation for the container sizes of gallon, half- gallon, and quart. A rigorous explanation of the interpretation between the estimates derived from the two hedonic models was discussed. Using the Almost Ideal Demand System (AIDS), a matrix of own-price, crossprice, and expenditure elasticities was estimated involving various container sizes of white milk, flavored milk, carbonated soft drinks, bottled water, and fruit juices, using a cross-section of the 1999 HSD. We described price imputations and the handling of censored observations to develop the respective elasticities. These elasticities provided information about intra-product relationships (same product but different sizes), intrasize relationships (different products same container size), and inter-product relationships (different products and different sizes). This container size issue is unique in the extant literature associated with non-alcoholic beverage industry.

Thesis (Ph. D.)--University of Wisconsin--Madison, 1984. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 170-173).

Understanding the evolution of water demand is of paramount importance for countries that want to implement the correct water demand management strategies that aim at increasing water use efficiency. This paper analyses household water demand in the capital city of the Mpumalanga Province of South Africa, in order to develop a better understanding of residential water demand in developing country contexts. Using survey data from 526 households in the Mbombela Municipality of Mpumalanga, South Africa, we estimate the price and income elasticities of household water demand, and investigate the factors that drive water demand of households that are located in heterogenous income groups. Households in the study areas have the unique characteristic seen in developing countries of having access to several sources of water, such as tap, ground and rainwater, implying the possibility of substitution. We run different estimation strategies that range from OLS, 2SLS and instrumental variable approaches to identify the factors that influence urban water demand. The findings reflect that price and income elasticities vary across different household groups, with price elasticities ranging from -0.140 to -0.879 and income elasticities ranging from 0.172 to 0.628. Other statistically significant variables which drive household water consumption are household size, education level, use of water saving technologies, and the use of rainwater tanks and systems. A crucial finding in this study was that water saving technologies were revealed to reduce water consumption levels by between 28.3% to 43.4%, and we hence provide specific policy recommendations based upon this finding. Overall, the results from this study can contribute substantially towards the development of appropriate and sustainable water policy making in South Africa.