Thesis (Mathematics) -- University of Limpopo, 2010 / Forecasting electricity consumption is a challenge for most power utilities. In South Africa the anxiety posed by electricity supply disruption is a cause for concern in sustainable energy planning. Accurate forecasting of future electricity consumption has been identified as an essential input to this planning process. Forecasting electricity consumption has been widely researched and several methodologies suggested. However, various methods that have been proposed by a number of researchers are dependent on environment and market factors related to the scope of work under study making portability a challenge. The aim of this study is to investigate models to forecast short term electricity consumption for operational use and medium term electricity consumption for tactical use in the Ferrochrome sector in South Africa. An Autoregressive Moving Average method is suggested as an appropriate tool for operational planning. The Holt-Winter Linear seasonal smoothing method is suggested for tactical planning. Keywords: Forecasting, electricity consumption, operational planning, tactical planning, ARIMA, Holt-Winter Linear seasonal smoothing, Ferrochrome sector

In this study, 22 homes in Milton, Ontario had their electricity consumption monitored for between seven and 15 months, and they were provided access to their data via an online webportal. The webportal provided appliance-level and house-level data, allowed them to set consumption goals, and schedule when their appliances would be used. The households were chosen to participate because they had previously expressed interest in advanced smart meter grid technologies, and when contacted again by Milton Hydro, they agreed to participate in the study. The main question being asked in this research is: what effect, if any, does having access to one???s consumption data have on consumption? To investigate this question, consumption data from the monitoring period, and the previous year (the base year) were obtained from Milton Hydro and were used to determine how consumption changed between these two periods. The consumption data for the cooling months were weather normalized to account for increases in consumption that result from cooling the dwelling. Data regarding users??? engagement with the webportal, including how often they would login, for how long and what pages they were visiting, were collected from the webportal. An engagement index was adapted and refined from Peterson & Carrabis (2008), and along with the engagement data from the webportal, was used to calculate the engagement index. Data from two surveys were used to profile the households and to investigate their attitudes and behaviours towards electricity consumption. There were several key findings. First, engagement with the webportal was quite low; the engagement index (a value between zero and one) for the first three months the hub was open averaged 0.285 and ranged from 0 to 0.523. These numbers dropped by the end of the seventh month to an average engagement index of 0.163, and ranged from 0 to 0.341. The second key finding was that the hubs were not consistently conserving electricity; for the first three months, 10 of the 22 households had conserved electricity between the base year and monitoring period; at the end of the seventh month, this dropped to nine households. At the end of the third month, the change in consumption was an increase of 8.22%, and at the end of month seven it was an increase of 7.71%. The third finding was that there did not appear to be a connection between energy conserving attitudes and energy conserving behaviours. In the surveys, 12 households stated that their goal was to conserve electricity, however, of these 12, only four actually conserved electricity at the end of month seven. Finally, when comparing the engagement index with the change in consumption, there appeared to be only a weak, negative correlation between the variables. This weak correlation may be a result of two things: (1) a lack of engagement, which limits the ability to find correlation between engagement and change in consumption; (2) there is actually a weak relation between the two variables. Based on these findings, some recommendations are put forth, specifically about how to engage householders with the webportal. Suggestions include getting applications for mobile devices, and delivering electricity saving tips to households via e-mail, text message, and/or on the homepage of the portal. These tips could be given based on the season, or based on the goals that were set, and would encourage and explain to householders how to decrease consumption.

I combine quantitative data on minute-resolved electricity-consumption profiles and survey data with qualitative interviews of PV adopters to create a holistic understanding of how PV adoption influences behavioral change of electricity use. In particular, I examine the information and heuristics consumers use to make energy-related choices and evaluate how consumption behavior affects the total amount and timing of electricity use. Consumption behavior post adoption can significantly alter the environmental benefits of solar PV. Post-adoption changes such as decreases in energy consumption or load shifting from times of high peak demand to times of lower peak demand increase the amount of solar PV generation that is exported to the grid. Higher outflows may reduce the need for less efficient peaking generation units during peak demand, particularly in the summer when solar PV is at its highest generation capacity and electricity demand is greatest. I find that PV adoption does trigger increases in awareness of electricity use. However, while adopters report small or insignificant decreases in household consumption post-adoption, examination of actual records shows both significant increases and decreases in consumption post-PV adoption at the household level. I explain this seeming discrepancy by noting that these households were already energy-conscious prior to PV adoption and had newer, more energy efficient homes, which could offset effects of increased awareness. Supporting this, a majority of respondents considered PV adoption as one action within a larger electricity conservation campaign initiated prior to system adoption. Because they had already implemented several energy efficiency measures, respondents could not easily identify additional ways to reduce electricity use. Most respondents have a method of monitoring consumption, but their attentiveness to monitoring declines after installation-- which could explain the awareness gap as well as the consumption increase. In addition, exogenous factors such as the purchase of an electric vehicle and changes in household size may explain increases in consumption. While I find changes in total consumption after adoption of solar PV at the individual household level, the aggregate mean consumption for all households is just 1.0% but the change in means is insignificant. / text

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Transportation is one of the areas where Sweden could not yet manage to reduce the CO2 emissions. One solution that has been suggested to reduce the CO2 emissions in this sector is through the mass adoption of electric vehicles (EVs). However, mass EV adoption brings complications with it. Drivers behavior is a critical aspect since people often charge their car at home after work. This could negatively affect the evening load peak and thus cause a high impact on the electricity system. A survey was sent out to current private EV owners in Sweden, to learn about their charging schedules, driving patterns and battery capacity. 226 of 403 replied to the survey which gave a survey reply rate of 56 %. The goal of this work was to estimate the future adoption of EVs, based on the current trends and national targets in order to develop different scenarios. With the scenarios in mind, the projected consumption of EVs for different periods of the day, the magnitude and time of the peak load as well as the overall consumption and CO2 reduction per year were calculated. Three scenarios were analyzed with 96 000, 650 000 and 1 000 000 electric vehicles where 25 % are defined to be running entirely on electricity in the middle and high penetration scenario since even plug-in hybrid electric vehicles, PHEV where included. The scenarios are estimated as the possible situation in 2030 and a simulation is done in MATLAB for summer and winter cases as well as weekdays and weekends. Results showed that the charging pattern of the EV drivers would cause a peak load at around 20.00 where the peak load from the overall household consumptions also takes place. The highest consumption takes place during the weekend cases but there were no significant difference between summer and winter. For example the peak consumption of the EVs was 150 MWh during winter and weekends at 20.00. The annual consumption of the EVs would be 238 GWh, 342 GWh and 616 GWh for the low, middle and high penetration scenario. By analyzing the current installed power of renewable energy sources in Sweden, it was found that the demand for EVs could be met by renewables entirely today. It was also found that using EVs instead of conventional fossil fueled cars can save up to 264 Mton CO2 for the low penetration scenario, 447 Mton for the middle penetration scenario and 688 Mton for the high penetration scenario. Different assumptions could have caused deviation from the actual result and it was found during the implementation of the simulation that the survey questions could be improved for future surveys. It was concluded that mass adoption of EVs is possible in terms of electricity production and installed power. However, increase in the evening peak led to the conclusion that balancing of the grid is necessary for example through Vehicle-to-grid (V2G), controlled charging or energy storage. Keywords: MATLAB, electricity consumption, EV, CO2 emissions, simulation, 2030, Scenario, penetration level

By using IT-solutions, the flexibility on the demand side in the electrical systems could be increased. This is called demand response and is part of the larger concept called smart grids. Previous work in this area has concerned the utilization of demand response by grid owners. In this thesis the focus will instead be shifted towards the electrical companies that have balance responsibility, and how they could use demand response in order to make profits. By investigating electrical appliances in hourly measured households, the business value from decreasing electrical companies’ power imbalances has been quantified. By an iterative simulation scheme an optimal value was found to be 977 SEK/year and appliance. It could however be shown that the value became larger for energy inefficient households, and that such consumers’ participation in a demand response market would be prioritized ahead of other measures like isolating walls is rather unlikely. Thermal appliance whose load depend on the outdoor temperature are less valuable for demand response during the summer months, and the annual value would increase if less seasonally dependent appliances were used. Additionally, by increasing the market price amplitudes and the imbalance price volatility, it could be shown that the potential for such demand response markets is larger in e.g. the Netherlands and Germany.

This thesis investigates the influence of energy visualization on hot water consumption, as well as builds up the framework for the analysis of electricity consumption, of multi-apartment buildings in Sweden, . 115 apartments in Nynäshamn have been scheduled to be equipped with feedback visualization, through the use of television sets, allowing the monitoring of electricity and hot water consumption on a monthly basis. One year of consumption data prior to this feedback introduction was acquired for statistical analysis. The results were then displayed and analysed, allowing for the composition of a generalized conclusion whilst revealing the need for further investigation and future work. To achieve this end, clustering was performed on the 115 apartments according to the following characteristics: Number of tenants per apartment, Area of the apartment, Location of the apartment, and age of the tenants. Principal Component Analysis was used to select dominant characteristics, through eliminating highly correlated components, after which trend analysis was performed on each of the separate clusters revealing a seasonal change model. Finally, a Multivariate Analysis of Variance utilized on the paired clusters to identify any significant change, along with post-analysis tests to specify the groups in which significant change was detected, is presented to be applied in future work. The preliminary results clearly show that the characteristic data can be grouped into three distinct clusters of which the consumption trend of hot water consumption is distinct. Moreover the data reveals a correlation between the apartment’s characteristics and its hot water consumption. However further monitoring and data collection will be required before any strong trend can be identified, as well as power analysis will have to be applied to conclude any significant change. Nonetheless the initial results demonstrate promising signs that ought to be further investigated in the future. 2

Energy consumption and correlated greenhouse gas emissions are a big global problem. It affects all parts of society, and each industrial sector must work toward reducing itscarbon footprint. This thesis details the research of different methods to model the energyconsumption of video streaming, and works towards creating a final model. The videostreaming process is broken down into a core process consisting of head-end, distribution,transmission, and terminals. The process that contributes the most to energy consumptionat the head-end is found to be video encoding. This thesis explores the energy consumption of video encoding in depth and how it is affected by parameters such as hardware,codec choice, codec preset selection, and video details such as resolution, framerate, andduration, but these parameters are found to be insufficient to accurately model the energyconsumption of video encoding. In distribution and transmission, the highest contributor is found to be content delivery networks. The energy consumption of content deliverynetworks is investigated however no appropriate model is found. For terminals, the mostimportant factor is the kind of terminal used. The energy consumption of televisions, desktop computers, laptops, and mobile terminals is investigated, and models are presented foreach. The thesis also discusses the different models, their advantages, and their shortcomings. Additionally, an application to visualize features of the model is presented.

Yes / Detailed electricity load profile for domestic building is an important requirement for the accurate analysis of demand side management. The use of electrical appliances within domestic buildings varies significantly with respect to time, mainly in accordance with the activity and behaviour of the occupants. This paper presents results from a monitoring study of electrical energy consumption profiles for One UK household (two adults with children). Measurements for whole household electricity consumption have been obtained over a period of ten months. They were all obtained at one minute interval. Monthly energy consumptions, daily and overall profiles were derived for this household type from the monitored data. It is intended that the results presented in this paper can be used in the quest for a precise forecast method for electricity consumption for occupants living in the same type of household in the UK. This will allow greater confidence in the sizing of, e.g., adopting renewable energy sources in this type of household. Further investigation is needed for a large sample of households to improve the understanding of monitoring high resolution domestic energy consumption. / MSCRC

Yes / Electricity Suppliers in competitive electricity markets commonly respond to prices changes which are fluctuating over time, but most consumers respond to the price changes as reflected on their electricity bills. Almost all consumers pay fixed tariffs for their consumption without distinctions based on usage time, so these consumers have had no incentives to reduce their use during the peak times. This paper aims to analyze the influence of different tariff schemes on consumer behaviours in UK domestic buildings. A realistic half hourly electricity load profile for different types of UK households that based mainly on public reports and statistics has been generated. This load profile data were used to help calculate the expected change in consumers' bills under standard tariffs offered from different suppliers to what the cost of electricity would be under time varying tariff (economy7 tariff) and to estimate of how much consumers would shift their load in response to price changes without changing total consumption, for which the results are presented and discussed / MSCRC