En gårdsbiogasanläggning har flera stora interna energiförbrukare. Tidigare forskning har visat att energibehovet för endast omblandning i en biogasanläggning kan uppgå till 1 % av biogasens energiinnehåll vilket är oerhört högt. En omrörningsstudie har genomförts där data som specifik omrörningseffekt (W/m3) och energiförbrukning (kWh/dag) har inhämtats ifrån verkliga fall och sedan jämförts emot varandra. Samband mellan större rötkammarvolymer och högre energiförbrukning per dag för omrörning kunde finnas. Samtidigt som kopplingar mellan mindre anläggningar och högre specifik omrörningseffekt (W/m3) också kunde finnas. En rötkammares värmebehov ligger teoretiskt på cirka 33 % för termofila processer och 20 % för mesofila processer av den teoretiska energiproduktionen för en anläggning med en rötkammare på 750 m3. En planerad anläggning med rötkammarvolym på cirka 3000 m3 och specifik omrörningseffekt på 22 W/m3, borde ha ett högre elbehov per år än 100 000 kWh/år. / A farm biogas plant has several large internal energy consumers. Previous research has shown that the energy for only mixing in a biogas plant may reach 1% of the biogas energy content which is extremely high. A mixing study was performed where data specific stirring power (W/m3) and energy consumption (kWh/day) has been obtained from real cases and then compared against each other. Correlation between larger reactor volumes and higher energy consumption per day for agitation could be. While connections between smaller plants and higher specific stirring power (W/m3) could also be. A digester heating demand is theoretically at around 33% for thermophilic processes and 20% for mesophilic processes of the theoretical energy output for a plant with a digester of 750 m3. A planned facility with reactor volume of approximately 3000 m3 and specific stirring power at 22 W/m3, should have a higher electricity demand per year than 100 000 kWh/year.

This paper examines the Environmental Kuznets Curve (EKC) hypothesis and tests for causality using Dynamic Ordinary Least Squares (DOLS) and the Vector Error Correction Model (VECM). There is evidence of long-run relationships in the three models under consideration. The Dynamic Ordinary Least Squares (DOLS) finds no evidence to support the existence of an environmental Kuznets curve (EKC) hypothesis for Zambia in the long-run. The evidence from the long-run suggests an opposite of the Environmental Kuznets Curve (EKC), in that the results indicate a U-shaped curve relationship between income and carbon emission. The conclusion on causality based on the VECM is that there is evidence of neutrality hypothesis between either total electricity and income or between industrial electricity and income in the short-run Additionally, there is evidence of conservation hypothesis in the context of residential and agricultural electricity consumption.

Este trabalho tem como objetivo analisar a composição setorial do consumo de energia elétrica incorporado às exportações brasileiras em cada área de concessão de distribuição. Para tanto, foi elaborada uma matriz de insumo-produto inter-regional e coeficientes setoriais de consumo de energia elétrica por área de concessão. Foi constatado que o consumo de energia elétrica incorporado às exportações é maior nos setores industriais e que a produção destinada à exportação é mais eletrointensiva que a produção destinada ao consumo interno em 37 das 58 áreas de concessão analisadas. / The objective of this dissertation is to analyze the sectorial composition of the electric energy consumption used in the production of exported goods and services in each electricity distribution concession area in Brazil. To accomplish this goal, an input-output matrix was elaborated, along with sectorial coefficients of electric energy intensity. The results indicate that the sector which consumes the most energy in their exports is the industrial sector. There was also an indication that Brazilian exports consume more electric energy throughout their productive structure than the production absorbed internally in 37 of the 58 electricity distribution concession areas.

<p>Within the next years, the amount of consumption data will increase rapidly as old meters will be exchanged in favor of meters with hourly remote reading. A new refined supervision system must be developed. The main objective of this thesis is to investigate mathematical methods that can be used to find incorrect hourly measurements in district heat and electricity consumption, for each consumer. </p><p>A simulation model and a statistical model have been derived. The model parameters in the simulation model are estimated by using historical data of consumption and outdoor temperature. By using the outdoor temperature as input, the consumption can be simulated and compared to the actual consumption. Faults are detected by using a residual with a sliding window. The second model uses the fact that consumers with similar consumption patterns can be grouped into a collective. By studying the correlation between the consumers, incorrect measurements can be found. </p><p>The performed simulations show that the simulation model is best suited for consumers whose consumption is mostly affected by the outdoor temperature. These consumers are district heat consumers and electricity consumers that use electricity for space heating. The fault detection performance of the statistical model is highly dependent on finding a collective that is well correlated. If these collectives can be found, the model can be used on district heat consumers as well as electricity consumers.</p>

The electricity system is particularly susceptible to climate change due to the close interconnectedness between not only electricity production and consumption to climate, but also the interdependence of many European countries in terms of electricity imports and exports. This study provides a country based relative analysis of a number of selected European countries’ electricity system vulnerability to climate change. Taking into account a number of quantitative influencing factors, the vulnerability of each country is examined both for the current system and using some projected data. Ultimately the result of the analysis is a relative ranked vulnerability index based on a number of qualitative indicators. Overall, countries that either cannot currently meet their own electricity consumption demand with inland production (Luxembourg), or countries that experience and will experience the warmest national mean temperatures, and are expected to see increases in their summer electricity consumption are found to be the most vulnerable for example Greece and Italy. Countries such as the Czech Republic, France and Norway that consistently export surplus electricity and will experience decreases in winter electricity consumption peaks were found to be the least vulnerable to climate change. The inclusion of some qualitative factors however may subject their future vulnerability to increase. The findings of this study enable countries to identify the main factors that increase their electricity system vulnerability and proceed with adaptation measures that are the most eective in decreasing vulnerability.

Within the next years, the amount of consumption data will increase rapidly as old meters will be exchanged in favor of meters with hourly remote reading. A new refined supervision system must be developed. The main objective of this thesis is to investigate mathematical methods that can be used to find incorrect hourly measurements in district heat and electricity consumption, for each consumer. A simulation model and a statistical model have been derived. The model parameters in the simulation model are estimated by using historical data of consumption and outdoor temperature. By using the outdoor temperature as input, the consumption can be simulated and compared to the actual consumption. Faults are detected by using a residual with a sliding window. The second model uses the fact that consumers with similar consumption patterns can be grouped into a collective. By studying the correlation between the consumers, incorrect measurements can be found. The performed simulations show that the simulation model is best suited for consumers whose consumption is mostly affected by the outdoor temperature. These consumers are district heat consumers and electricity consumers that use electricity for space heating. The fault detection performance of the statistical model is highly dependent on finding a collective that is well correlated. If these collectives can be found, the model can be used on district heat consumers as well as electricity consumers.

A survey of residential solar photovoltaic (PV) adopters in Texas was administered and the results are presented and discussed. A 40% response rate was achieved and 365 complete responses were received. In addition to demographics, the survey uncovered aspects related to the decision-making process, information search, financial attractiveness of PV, and post-installation experience. Peer-effects did not have a large influence on the adoption of residential PV in Texas, but the potential for increasing the number of communication/information channels to increase the adoption rate of PV exists. Adopters experienced little uncertainty at the time of PV installation because sufficient dependable information was available during the search process. Overall, they are satisfied with PV. Contextual factors, such as income and the ability to purchase a PV system rather than lease one, influence behavior. Those who decreased electricity consumption post-adoption were more motivated to adopt by environmental concern and a general interest in energy than those who increased electricity consumption post-adoption. Those who experienced behavior changes also experienced an increase in awareness of electricity use post-adoption, while those who did not experience a behavior change reported no change in awareness post-adoption. Change in awareness of electricity use is less dependent on the attitudinal and contextual factors, such as environmental concern, motivation for adoption, age, and income, that influence consumption change. The potential for further analysis of the survey results is great and will likely yield additional conclusions about the consequences of the adoption of PV. Coupling the survey results with historical electricity bill data will yield stronger conclusions about behavior change. Surveying geographical areas outside of Texas is recommended. / text

Electricity costs in South Africa have risen steeply; there are a number of factors that have contributed to this increase. The increased costs have a considerable inuence on the mines and mining sector in general. It requires considerable planning to assist mines in such management. The present study addresses the development of a way to predict both electricity consumption and costs, which general involves a large range of personnel. The majority of planning personnel can be more usefully employed in other ways. The goal is not to replace such planners but make them more task e_ective. Automation, which will reduce their workload, may have little or no e_ect on performance. In some cases, however, automation may produce better results. There is a complex system to be analysed in the prediction of electricity consumption and costs. The existing prediction methodology is investigated in this study; the investigation highlights the need for a new methodology. The new method should be automated, easier to use and more accurate. Such a model is developed. The new prediction methodology extracts data from the monthly Eskom bills and stores it in a database. The data is grouped according to a new model and then normalised. An arti_cial neural network is used to \learn" the dynamics of the data to calculate new future electricity consumption. Electricity costs are predicted by multiplying the predicted electrical consumption with a calculated factor based on cost per electricity unit of the previous year with the expected increase added. The new methodology is integrated in a commercial energy management platform named Management Toolbox, which o_ers a range of functionality. In this study the prediction of electricity consumption and costs are implemented. The implementation is executed with simplicity in mind and care is taken to present the user with the optimal amount of data. The performance of the electricity consumption prediction is sensitive to production changes and the quality of the data history. Performance of the electricity costs prediction model is an improvement over the existing prediction method. The proposed methodology has greater accuracy and uses less personnel, which can lead to using most of the personnel on more important tasks. / Thesis (MIng (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2013.

Electricity costs in South Africa have risen steeply; there are a number of factors that have contributed to this increase. The increased costs have a considerable inuence on the mines and mining sector in general. It requires considerable planning to assist mines in such management. The present study addresses the development of a way to predict both electricity consumption and costs, which general involves a large range of personnel. The majority of planning personnel can be more usefully employed in other ways. The goal is not to replace such planners but make them more task e_ective. Automation, which will reduce their workload, may have little or no e_ect on performance. In some cases, however, automation may produce better results. There is a complex system to be analysed in the prediction of electricity consumption and costs. The existing prediction methodology is investigated in this study; the investigation highlights the need for a new methodology. The new method should be automated, easier to use and more accurate. Such a model is developed. The new prediction methodology extracts data from the monthly Eskom bills and stores it in a database. The data is grouped according to a new model and then normalised. An arti_cial neural network is used to \learn" the dynamics of the data to calculate new future electricity consumption. Electricity costs are predicted by multiplying the predicted electrical consumption with a calculated factor based on cost per electricity unit of the previous year with the expected increase added. The new methodology is integrated in a commercial energy management platform named Management Toolbox, which o_ers a range of functionality. In this study the prediction of electricity consumption and costs are implemented. The implementation is executed with simplicity in mind and care is taken to present the user with the optimal amount of data. The performance of the electricity consumption prediction is sensitive to production changes and the quality of the data history. Performance of the electricity costs prediction model is an improvement over the existing prediction method. The proposed methodology has greater accuracy and uses less personnel, which can lead to using most of the personnel on more important tasks. / Thesis (MIng (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2013.

Este trabalho tem como objetivo analisar a composição setorial do consumo de energia elétrica incorporado às exportações brasileiras em cada área de concessão de distribuição. Para tanto, foi elaborada uma matriz de insumo-produto inter-regional e coeficientes setoriais de consumo de energia elétrica por área de concessão. Foi constatado que o consumo de energia elétrica incorporado às exportações é maior nos setores industriais e que a produção destinada à exportação é mais eletrointensiva que a produção destinada ao consumo interno em 37 das 58 áreas de concessão analisadas. / The objective of this dissertation is to analyze the sectorial composition of the electric energy consumption used in the production of exported goods and services in each electricity distribution concession area in Brazil. To accomplish this goal, an input-output matrix was elaborated, along with sectorial coefficients of electric energy intensity. The results indicate that the sector which consumes the most energy in their exports is the industrial sector. There was also an indication that Brazilian exports consume more electric energy throughout their productive structure than the production absorbed internally in 37 of the 58 electricity distribution concession areas.