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1981, Energy
A forecasting model is constructed for the analysis of the long-run electricity and natural gas requirements of the commercial sector of a regional economy. The methodology combines an econometric model to generate baseline forecasts, a detailed end-use model to evaluate conservation and regulatory program impacts, and a disaggregated economic/demographic model to provide internally consistent forecasts of commercial employment and other relevant economic conditions. The model is estimated and used to provide forecasts of energy use and regulatory impacts in San Diego County, California.
Energy
Forecasting the residential natural gas demand for large groups of buildings is extremely important for efficient logistics in the energy sector. In this paper different forecast models for residential natural gas demand of an urban area were implemented and compared. The models forecast gas demand with hourly resolution up to 60 h into the future. The model forecasts are based on past temperatures, forecasted temperatures and time variables, which include markers for holidays and other occasional events.
Policy Research Working Paper …, 2009
Statistical journal of the United Nations economic commission for Europe, 1984
Energy price changes over the last decade have brought the relationship between energy price and energy demand into focus. Knowledge of this relationship is important for both the formulation of energy conservation policies and energy demand forecasts. The needs for price and energy statistics to assess the price impacts on demand for energy and the individual energy goods have increased. These needs can be considered on the basis of the most common types of energy demand models and their uncertainty. A meaningful energy demand model requires both energy and price data as regards the most important energy consuming sectors. Separate price information should be provided on each of the major energy goods. The uncertainty associated with energy demand models and their use in forecasting ha~ created a need for up-to-date information enabling frequent evaluation of the models. This concerns both energy and price statistics, and a regular provision of preliminary figures presents a challenge to energy statisticians.
Energy Economics, 2014
This paper develops an econometric forecasting system of energy demand coupled with engineering-economic models of energy supply. The framework is used to quantify the energy and environmental impacts of renewable and natural gas based electricity power generation in California, considering the role of ongoing energy conservation efforts and incorporating different natural gas price scenarios over the forecast horizon (2011-2035). The results indicate that, relative to a business-as-usual scenario of continuing to rely on imported electricity to meet future demand, California's renewable portfolio standard (RPS) of 33% renewables by 2020 will increase electricity rates by over 10%. However, the RPS will also provide substantial annual savings in carbon dioxide emissions, equal to 40 million metric tons in 2020. Continuing non-price induced energy conservation at the historical rate will only result in a marginal reduction in electricity rates, although lower electricity use means that substantial savings are nonetheless achieved in electricity expenditures. In addition, continuing trend energy conservation leads to substantial savings in carbon dioxide emissions. Like the RPS, developing domestic natural gas generation also leads to rate increases and reductions in carbon dioxide emissions (relative to the baseline). However, these impacts are minor compared to the RPS scenario.
1991
In this study; sponsored by the California Energy Commission (CEC), we used a new end-use load shape estimation technique to develop a database of commercial sector end-use load shapes and energy-use intensities (EUls) for the CEC's commercial energy and peak load forecasting models. The technique relied on a reconciliation of whole-building hourly electricity load data to energy simulations developed from an analysis of survey data. The technique was applied to four building types (schools, colleges, health, and lodging) and resulted in reconciled hourly electricity load shapes for eight end uses. The end uses included cooling, ventilation, lighting, cooking, refrigeration, water heating, hospital equipment, and miscellaneous equipment. Ventilation and cooling load shapes were estimated separately for four climate regions in southern California. The load shapes were aggregated to produce annual end-use EUls, and twelve monthly end-use load shapes for three day types (peak, standard, and non-standard). The end-use EUls were then adjusted to account for observed end-use saturations in the population, the effects of price and technological change, and, most importantly, the impacts of the first generation of California building and appliance standards.
Resource and Energy Economics, 1994
This paper describes an empirical method for estimating the effect of expected prices on energy demand. Data for expected oil prices are compiled from forecasts for real oil prices. The effect of expectations on energy demand is simulated with an expectation variable that proxies the return on investment for energy efficient capital. Econometric results indicate that expected prices have a significant effect on energy demand in the US between 1975 and 1989. A model built from the econometric results indicates that the way in which consumers anticipate changes in energy prices that are generated by a carbon tax affects the quantity of emissions abated by the tax.
1982
Abstract Total electricity use and cooling loads for a three month cooling season (July–September, 1980) in single-family detached houses in Davis, California, are estimated and compared with measured data. Total electricity use is estimated by predicting cooling loads and appliance electricity use using a technique approximating a relatively low cost audit program. Cooling loads are estimated using an interpolation model to simplify application of the DOE-2.1 A energy use computer model.
This paper briefly outlines the methodology used to develop a new model of energy demand. It contains a description of the model equations used to determine the sectoral demand for energy. It details the set of engineering relationships in the electricity generation block, while then detailing the determination of carbon dioxide (CO2) emission levels. It then outlines the links between this energy model and the main HERMES macroeconomic model. It looks at the performance of the energy model within sample, and contains some preliminary estimates of the links between energy prices and taxes and the level of carbon dioxide emissions. Appendix 1 details the notation used in the databank, while Appendix 2 lists the full set of equations included in the current version of the energy model.
The Energy Journal, 2012
Impacts of a range of policy scenarios on end-use energy demand are examined using a coupling of MARKAL, an energy system model with extensive supply and end-use technological detail, and Inforum LIFT, a large-scale model of the U.S. economy with inter-industry, government, and consumer behavioral dynamics. Responses in end-use energy demand are the result of energy efficiency improvements, fuel switching, and indirect economy-wide impacts. Carbon emissions reductions attributed to end-use demand response are analyzed and compared to carbon emissions reductions attributed to changes in the electric sector. Scenarios with the greatest impacts are a carbon tax case, resulting in a shift away from coal generation in the electric sector, and a normative case using a 7% discount rate for end-use technology investment decisions, resulting in increased adoption of energy efficient technology. In the course of addressing the specific EMF 25 scenarios and specified assumptions, a number of interesting issues were identified for follow-on analyses.
Policy Research Working Papers, 2016
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Advances in Business and Management Forecasting, 2011
This thesis presents a forecasting model for propane consumption within the residential sector. In this research we explore the dynamic behavior of different variables that affect the propane consumption and develop a forecasting model. The significant factors that had an impact on propane consumption in houses were heating degree days of that area, wind speed, precipitation and the size of the houses. However in the case of mobile homes, only the heating degree days had significance. The behavior of the customers was assumed to be static. This model is based on multiple regression methods. The data was collected from a local propane company in West Texas. Different combinations of months were used in this model to study propane consumption behavior for each month. These different studies were used to generate the final forecasting model. As propane consumption was low for the months from June to September, the best results were obtained when the data for the months from October through May was used for analysis. The results indicate that the forecasting model provides a potentially useful forecast.
Commercial electricity usage exceeds that of industrial usage and is almost as large as residential electricity consumption in the United States. In this study, regional economic, demographic, and climatic data are used to analyze commercial electricity demand in the United States. Results indicate that total commercial demand for electricity is negatively related to price. In addition, the number of businesses and service income positively affect electricity demand for commercial use. The results are similar for equations estimated for kilowatt-hours demanded per business. The regional dummy variables exhibit different signs, which may occur due to climate factors because warm weather regions experience greater volumes of cooling degree-days, while cool weather regions observe larger amounts of heating degree-days. Although coefficients for the price of natural gas are positive, they do not satisfy the 5-percent significance criterion. The latter suggests that natural gas may not be a substitute good for electricity within the commercial sector of the U.S. economy.
Energy, 1990
Prior to 1973, most industries--including electric utilities-forecasted growth using rather straightforward time-trend approaches.
2008
The paper develops an Applied General Equilibrium (AGE) model for the estimation of energy demand and applies it to the Spanish Economy. The price system is based in the classical (Sraffian) theory of prices of production. The quantity system is based on the Keynesian principle of effective demand supported by broad energy multipliers. Both systems have been adapted to the specificities of energy industries. The model is dynamic in nature since output and technology are evolving through time. Energy technical coefficients are declining at a specific rate that may be speeded up or slowed down after changes in prices of the different sources of energy. The "tendencies" and "elasticities" implied are computed by calibration and econometric methods.
Procedia Computer Science, 2015
This work describes a methodology for long-term electricity demand forecast in the residential sector. The methodology has been used in the power market studies of some Brazilian distribution utilities. The methodology is based on decomposition of the total electricity residential consumption in three components: average consumption per consumer unit, electrification rate and number of households. Then, the forecast for the total electricity consumption in residential sector is the product of forecasts for these three components. The prediction for the number of households is based on demographic models while the future trajectory of the electrification rate is defined by the targets for achieving the universal access to electricity. The product of these two components provides a forecast to the number of residential customers. The average consumption per unit consumer depends on the macroeconomic scenarios for GDP, average household income and income distribution. The proposed methodology provides a framework to integrate macroeconomic scenario, demographic projection and assumptions for ownership and efficiency of electric appliances in a long-term demand forecast. In order to illustrate the application of the proposed methodology, this paper presents a ten-year demand forecasts for the residential sector in Brazil.
Journal of Industrial Ecology, 2010
The absence of detailed information on residential energy end use characteristics for the United States has in the past presented an impediment to the effective development and targeting of residential energy efficiency programs. This article presents a framework for modeling space heating, cooling, water heating, and appliance energy end uses, fuels used, and carbon emissions at a zip code–level resolution for the entire United States. It combines a regression-based statistical model derived from Residential Energy Consumption Survey data with U.S. census 2000 five-digit zip code level information, climate division–level temperature data, and other sources. The results show large variations in energy use characteristics both between and within different regions of the country, with particularly notable differences in the magnitude of and distribution by fuel of residential energy use in urban and rural areas. The results are validated against residential energy sales data and have useful implications for both residential energy efficiency planning and further study of variations in use patterns.
Jurisdictions throughout the world are contemplating greenhouse gas (GHG) emission mitigation strategies that will enable meeting long-term GHG targets; many jurisdictions are now focusing on the 2020-2050 timeframe. We conduct an inter-model comparison of nine California statewide energy models with GHG mitigation scenarios to 2050 to better understand common insights across models, ranges of intermediate GHG targets (i.e. for 2030), necessary technology deployment rates, and future modeling needs for the state. The models are diverse in their representation of the California economy: across scenarios with deep reductions in GHGs by 2050, annual statewide GHG emissions are 8-46% lower than 1990 levels by 2030 and 59-84% by 2050; the largest cumulative reductions occur in scenarios that favor earlier reductions; non-hydroelectric renewables account for 30%-54% of all electricity generated for the state in 2030 and 59-89% by 2050; the transportation sector is decarbonized using a mix of energy efficiency gains and alternative-fueled vehicles; and bioenergy is directed towards the transportation sector, accounting for a maximum of 40% of transportation energy by 2050. Models suggest that without new policy, emissions from other non-energy sectors and from high-global-warming-potential gases may exceed California's 2050 GHG goal. Finally, high priority areas of future model development include: implementation of uncertainty analysis, improved representation of economic impacts and logistical feasibility of given scenarios, simultaneous modeling of criteria and GHG emissions, and greater modeling of interactions between two or more specific policies.
Energy Economics, 2007
This study develops a statistical model of industrial US natural gas consumption based upon historical data for the 1958-2003 period. The model specifically addresses interfuel substitution possibilities and changes in the industrial economic base. Using a relatively simple approach, the framework can be simulated repeatedly with little effort over a range of different conditions. It may also provide a valuable input into larger modeling exercises where an organization wants to determine long-run natural gas prices based upon supply and demand conditions.
18th International Conference and Exhibition on Electricity Distribution (CIRED 2005), 2005
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