Dissertations / Theses on the topic 'Orecasting of electricity consumption'
To see the other types of publications on this topic, follow the link: Orecasting of electricity consumption.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'Orecasting of electricity consumption.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
1
Крамаренко, Семен Віталійович. "Прогнозування електроспоживання на базі вейвлетів." Bachelor's thesis, КПІ ім. Ігоря Сікорського, 2019. https://ela.kpi.ua/handle/123456789/28917.
Full textAbstract:
Сучасні тенденції на ринку електроніки вимагають пошуку
комплексних рішень по прогнозуванні електроспоживання локальних
об’єктів. Тому, є актуальним дослідження методів, які при мінімальних
розрахунках будуть проводити прогнозування з максимальною точністю.
У дипломній роботі були досліджені існуючи методи прогнозування
електроспоживання локального об’єкту. Було з’ясовано, що на сьогоднішній
день існує два основні методи прогнозування електроспоживання, які
передбачають ситуацію з істотними стрибками електроспоживання, а саме:
метод прогнозування електроспоживання за допомогою нейронних мереж і
метод прогнозування електроспоживання за допомогою вейвлет-
перетворень. Було обрано метод прогнозування за допомогою вейвлетів,
оскільки він має ряд переваг перед методом прогнозування за допомогою
нейронних мереж.Modern trends in the electronics market require the search for comprehensive solutions for forecasting the local consumption of electricity. Therefore, it is actual research of methods, which at the minimum calculations will carry out forecasting with maximum accuracy. In the work, existing methods of forecasting the local consumption of electricity were investigated. It was found that to date there are two main methods of forecasting electricity consumption, namely: the method of forecasting electricity consumption with the help of neural networks and the method of predicting power consumption using wavelet transformations. Wavelet forecasting method was chosen because it has several advantages over the prognostication method using neural networks.
2
GHORBANI, SONIYA. "Anomaly Detection in Electricity Consumption Data." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-35011.
Full textAbstract:
Distribution grids play an important role in delivering electricityto end users. Electricity customers would like to have a continuouselectricity supply without any disturbance. For customerssuch as airports and hospitals electricity interruption may havedevastating consequences. Therefore, many electricity distributioncompanies are looking for ways to prevent power outages.Sometimes the power outages are caused from the grid sidesuch as failure in transformers or a break down in power cablesbecause of wind. And sometimes the outages are caused bythe customers such as overload. In fact, a very high peak inelectricity consumption and irregular load profile may causethese kinds of failures.In this thesis, we used an approach consisting of two mainsteps for detecting customers with irregular load profile. In thefirst step, we create a dictionary based on all common load profileshapes using daily electricity consumption for one-monthperiod. In the second step, the load profile shapes of customersfor a specific week are compared with the load patterns in thedictionary. If the electricity consumption for any customer duringthat week is not similar to any of the load patterns in thedictionary, it will be grouped as an anomaly. In this case, loadprofile data are transformed to symbols using Symbolic AggregateapproXimation (SAX) and then clustered using hierarchicalclustering.The approach is used to detect anomaly in weekly load profileof a data set provided by HEM Nät, a power distributioncompany located in the south of Sweden.
3
Vadda, Praveen, and Sreerama Murthy Seelam. "Smart Metering for Smart Electricity Consumption." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2476.
Full textAbstract:
In recent years, the demand for electricity has increased in households with the use of different appliances. This raises a concern to many developed and developing nations with the demand in immediate increase of electricity. There is a need for consumers or people to track their daily power usage in houses. In Sweden, scarcity of energy resources is faced during the day. So, the responsibility of human to save and control these resources is also important. This research work focuses on a Smart Metering data for distributing the electricity smartly and efficiently to the consumers. The main drawback of previously used traditional meters is that they do not provide information to the consumers, which is accomplished with the help of Smart Meter. A Smart Meter helps consumer to know the information of consumption of electricity for appliances in their respective houses. The aim of this research work is to measure and analyze power consumption using Smart Meter data by conducting case study on various households. In addition of saving electricity, Smart Meter data illustrates the behaviour of consumers in using devices. As power consumption is increasing day by day there should be more focus on understanding consumption patterns i.e. measurement and analysis of consumption over time is required. In case of developing nations, the technology of employing smart electricity meters is still unaware to many common people and electricity utilities. So, there is a large necessity for saving energy by installing these meters. Lowering the energy expenditure by understanding the behavior of consumers and its correlation with electricity spot prices motivated to perform this research. The methodology followed to analyze the outcome of this study is exhibited with the help of a case analysis, ARIMA model using XLSTAT tool and a flattening technique. Based on price evaluation results provided in the research, hypothesis is attained to change the behavior of consumers when they have better control on their habits. This research contributes in measuring the Smart Meter power consumption data in various households and interpretation of the data for hourly measurement could cause consumers to switch consumption to off-peak periods. With the results provided in this research, users can change their behavior when they have better control on their habits. As a result, power consumption patterns of Smart electricity distribution are studied and analyzed, thereby leading to an innovative idea for saving the limited resource of electrical energy.+91 9908265578
4
Mangisa, Siphumlile. "Statistical analysis of electricity demand profiles." Thesis, Nelson Mandela Metropolitan University, 2013. http://hdl.handle.net/10948/d1011548.
Full textAbstract:
An electricity demand profile is a graph showing the amount of electricity used by customers over a unit of time. It shows the variation in electricity demand versus time. In the demand profiles, the shape of the graph is of utmost importance. The variations in demand profiles are caused by many factors, such as economic and en- vironmental factors. These variations may also be due to changes in the electricity use behaviours of electricity users. This study seeks to model daily profiles of energy demand in South Africa with a model which is a composition of two de Moivre type models. The model has seven parameters, each with a natural interpretation (one parameter representing minimum demand in a day, two parameters representing the time of morning and afternoon peaks, two parameters representing the shape of each peak, and two parameters representing the total energy per peak). With the help of this model, we trace change in the demand profile over a number of years. The proposed model will be helpful for short to long term electricity demand forecasting.
5
Gupta, Pavan, University of Western Sydney, College of Law and Business, and School of Management. "Residential sector deregulation in the electricity industry : analysis of electricity consumption patterns." THESIS_CLAB_MAN_Gupta_P.xml, 2004. http://handle.uws.edu.au:8081/1959.7/744.
Full textAbstract:
The research presented in this thesis aims to improve our knowledge regarding the impact of privatisation and deregulation of public service type infrastructure industries. In recent years, Australia's industry reform policies have critically relied on rapid deregulation of major utilities such as telecommunication, gas and electricity. Although several industries have been deregulated in the last two decades, our understanding regarding the impact of deregulation on residential electricity market is still developing. In order to accomplish the research, about 400 residential customers were surveyed and their electricity consumption patterns (ECP)were monitored by installing special electronic meters. The findings are discussed in detail. As an implication to policy and practice there is an urgent need for a nation-wide standard,reshaping the practices of the electricity marketing and establishing a time-dynamic ECP monitoring system. Another important implication concerns the well-founded theories in micro-economic literature. This research has established that the price of the commodities and services charged by public service type utility suppliers should not be left entirely to the market forces concerned with demand - supply equilibrium. There is an urgent need to understand the role of different socio-economic segments in contributing to the economic efficiencies of public service type assets. More efficient segments should be equitably rewarded for their contributions rather than penalised perhaps due to the lack of their bargaining power.Doctor of Philosophy (PhD)
6
Gupta, Pavan. "Residential sector deregulation in the electricity industry : analysis of electricity consumption patterns /." View thesis, 2004. http://library.uws.edu.au/adt-NUWS/public/adt-NUWS20050225.104416/index.html.
Full textAbstract:
Thesis (Ph.D.) -- University of Western Sydney, 2004."A thesis presented to the University of Western Sydney, in partial fulfilment of the requirements of the degree of Doctor of Philosophy" Bibliography : leaves 273-285.
7
Daryanian, Bahman. "Scheduling of electricity consumption under spot prices." Thesis, Massachusetts Institute of Technology, 1989. http://hdl.handle.net/1721.1/14431.
Full textAbstract:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1989.Title as it appears in M.I.T. Graduate List, Feb. 1989: Optimal scheduling of electricity usage under spot prices.
Includes bibliographical references.
by Bahman Daryanian.
Ph.D.
8
Martin, William M. "PAY-AS-YOU-GO ELECTRICITY: THE IMPACT OF PREPAY PROGRAMS ON ELECTRICITY CONSUMPTION." UKnowledge, 2014. http://uknowledge.uky.edu/agecon_etds/29.
Full textAbstract:
Prepay or pay-as-you-go programs are an increasingly popular type of rate plan offered by electric utilities. Under these plans, ratepayers must keep a positive balance at all times to avoid being automatically disconnected, they are charged daily for their usage, and they are provided with a means to monitor their consumption. One of the suggested benefits of these plans is that they allow electricity consumers to better manage their usage. Using household level monthly usage data from customers enrolled in prepay programs at two Kentucky rural electric cooperatives, we investigate whether there is a change in consumption after these customers enrolled in the program. To address this question, we employ a fixed-effects model. The results of our model indicate that prepay customers reduce their consumption by an average of 11% after enrolling in the program. We also find that this response is larger during periods of high or low temperatures than during mild weather. Furthermore, we find evidence that the prepayment effect diminishes over the length of time that a customer is enrolled in the program.
9
Wallace, Eva-Lena. "How the Price of Electricity has Affected the Electricity Demand in the EU-27 During 1998-2008. : - Would an Environmental Tax on Electricity Reduce the Electricity Consumption and Increase the Share of Electricity Generated from Renewable Energy Sources?" Thesis, Umeå universitet, Handelshögskolan vid Umeå universitet (USBE), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-51328.
Full text
10
Kothapalli, Anil Kumar. "Short-Term electricity consumption prediction: Elområde 4, Sweden." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-87261.
Full textAbstract:
This Thesis work is part of course work for the Masters Program in Data Science at LTU. The focus of this work is mainly to review the literature published to identify state-of-art methodologies applied to predict short-term electricity consumption. This includes the exploration of features and models as well-as the discussion of the results attained. Identify opportunities to improve the forecast results for southern Elområde(bidding area)4, Sweden. The application of different modern methods to forecast electricity consumption has been studied and experimented with. This work adapted the CRISP-DM, a Data Science methodology.
11
Bowes, Robert Christopher. "Electricity use in the farm dairy." Thesis, University of Plymouth, 1989. http://hdl.handle.net/10026.1/2679.
Full textAbstract:
Dairy farmers suffered substantially increased energy bills during the 1970's, at a time of herd expansion and modernisation of equipment to allow bulk milk refrigeration and storage on the farm. Little was known of the levels of electricity use in the dairy farming sector, but extrapolations had suggested a figure of 300 to over 400 kWh/cow/annum. Farmers were requesting quantitative estimates for the potential of conservation equipment, particularly plate heat exchangers and heat recovery units. An energy audit of dairy farms in South Devon is described. Over a period of two years, data were collected relating to energy use by each of the major components of a milking parlour and dairy, for a range of parlour sizes, levels of production and the ambient conditions. Analysis revealed the factors most influencing variations in energy use. An equation was developed to describe the energy use by a bulk milk tank, given the level of production and the ambient temperature. The bulk tank accounted for some 40% of the total energy used. The bulk tank has been studied in detail. The stages of heat transfer from the milk to the chilled water and the resulting effects upon the ice bank have been modelled. Laboratory investigations were carried out to determine some parameters empirically. The model's limits, sensitivity and validation are reported. Typical levels and ranges of energy use are suggested. A mean of approximately 250 kWh/cow/annum resulted from the audit, but 200 kWh/cow/annum was achieved by the most economical of farms without resort to conservation equipment, and this level is proposed as a target for the conscientious farmer. The factors affecting energy use in the farm dairy are identified as political, environmental, technical and managerial and these are discussed. The farmer's influence has to be directed mainly at the last of these categories. Investment in energy conservation equipment should not be considered until consumption is down to the proposed target level.
12
Huang, Yizhang. "Estimating Response to Price Signals in Residential Electricity Consumption." Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-200633.
Full textAbstract:
Based on a previous empirical study of the effect of a residential demand response program in Sala, Sweden, thisproject investigated the economic consequences of consumer behaviour change after a demand-based time ofuse distribution tariff was employed. The economic consequences of consumers were proven to bedisadvantageous in terms of unit electricity price. Consumers could achieve more electricity bill saving throughstabilising their electricity consumption during peak hours, and this way bring least compromising of theircomfort level.In order to estimate the price elasticity of the studies demand response program, a new method of estimationprice elasticity was proposed. With this method, the intensity of demand response of the demand responseprogram was estimated in terms of price elasticity. Regression analysis was also applied to find out the priceincentives of consumer behaviour change. And the results indicated that the rise in electricity supply chargehardly contributes to load reduction, while the demand-based tariff constituted an advantageous solution on loaddemand management. However stronger demand response still
13
Reed, Theresa Louise. "The electricity consumption patterns of greater Boston non-profits." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/69382.
Full text
14
Silva, Jesús, Naveda Alexa Senior, Palma Hugo Hernández, Núẽz William Niebles, and Núẽz Leonardo Niebles. "Temporary Variables for Predicting Electricity Consumption Through Data Mining." Institute of Physics Publishing, 2020. http://hdl.handle.net/10757/652132.
Full textAbstract:
In the new global and local scenario, the advent of intelligent distribution networks or Smart Grids allows real-time collection of data on the operating status of the electricity grid. Based on this availability of data, it is feasible and convenient to predict consumption in the short term, from a few hours to a week. The hypothesis of the study is that the method used to present time variables to a prediction system of electricity consumption affects the results.
15
Williams, Christian A. J. "Time series models of annual electricity consumption, 1955-1997." Thesis, Loughborough University, 2004. https://dspace.lboro.ac.uk/2134/25031.
Full textAbstract:
The thesis deals with time series modelling of electricity demand data. Various techniques are used including ARIMA modelling, error correction modelling and cointegration analysis. Some forecasts have also been generated.
16
Mantel, Jessica Kirsten. "Investigating the potential for a user-driven electricity monitoring application to provide useful electricity consumption patterns." Master's thesis, University of Cape Town, 2017. http://hdl.handle.net/11427/25360.
Full textAbstract:
Conventional electricity usage monitoring involves complex data collection via costly and intrusive hardware installation. There is a perceived need for a simple and affordable tool that provides users with feedback about their electricity consumption without the hardware installation. This study involves the design and development of a user driven mobile and desktop application that provides users with information on electricity usage patterns and historical trends. The application was designed using Ionic Framework, a tool ideal for the design of hybrid applications that are compatible with both desktop Windows devices and mobile Android devices. The goal of the research will be that the user will track their appliance usage on the application whilst taking electricity meter readings at regular intervals to calculate appliance-specific consumption. The data is added to the mobile or desktop application, which then provides users with a comprehensive display of the electricity usage patterns and trends. The objective is to provide users with the information required so that they can start understanding their electricity consumption better and it is a first step towards empowering the user to make smart decisions at home that will reduce their electricity consumption. The USE (Usefulness, Satisfaction, Ease of Use and Ease of Learning) questionnaire was used to gather user experience feedback from participants about user experience. The USE questionnaire tests the perceived Usefulness, Satisfaction, Ease of Use and Ease of Learnability The 31 individuals who initially volunteered to take part in the study are all residents of the City of Cape Town Municipality, aged between 20 and 80 years old. Not all participants are home owners; some are tenants in their premises. The sample group was selected on a convenience basis, and social media group posts were also used to reach individuals with a potential interest in the study. The two motivating factors that were considered to identify individuals who could potentially have an interest in the study were cost saving and environmental impact. 21 volunteers completed the study and returned the USE questionnaire. The study findings showed that all participants believe that using the application helped them to better understand their electricity consumption.
17
Nicholson, Emma Leah. "Essays on restructured electricity markets." Connect to Electronic Thesis (ProQuest) Connect to Electronic Thesis (CONTENTdm), 2008. http://worldcat.org/oclc/436443232/viewonline.
Full text
18
Liddiard, Robert. "Characterising space use and electricity consumption in non-domestic buildings." Thesis, De Montfort University, 2012. http://hdl.handle.net/2086/6105.
Full textAbstract:
Energy used in the operation of the United Kingdom’s non-domestic buildings contributes 18% of national carbon dioxide emissions and reducing these is government policy. The use of electrical equipment in buildings is a major contributor to overall consumption, due to both its intrinsic energy consumption and the effects of incidental internal gains resulting from its operation. Knowledge of how and where consumption and internal gains occur in buildings is important in understanding the consumption characteristics of the building stock. The overall aim of this research was to improve the prediction of energy consumption in the non-domestic stock through the inference of appliance electricity consumption and resultant heat gains, for internal space uses of premises, as identified in UK property taxation data. To achieve this, the objectives were to: 1. Develop a method for inferring space usage in premises. 2. Infer values for the electricity consumption of appliances, and hence internal gains, for space uses within premises. 3. Apply the method to a dataset at the urban scale and use a suitable model to deduce the energy consumption. 4. Compare the results with measured data. Objectives 1 and 2 were achieved through analyses of detailed energy surveys of more than 300 non-domestic premises. By excluding equipment used for heating and cooling, both intrinsic electricity consumption and internal gains from appliances have been characterised for combinations of internal space use and premises activity type. For each combination, the characteristics include the energy intensity (kWh/m2/year) for: • overall appliance use • 14 end uses of appliances (e.g. lighting, catering, computers) • 18 groups of appliance activity descriptions (e.g. sales, office work, process) These characteristics were mapped onto subdivisions of space use, within premises, listed in property taxation data for a test urban area (City of Leicester). Using only 115 descriptions of space use, appliance consumption characteristics have been inferred for 91.5% of the measured internal floor area of the test dataset; this achieved the third objective. More than 80% of the floor area was identified using standard space use descriptions utilised in real estate taxation datasets. The total estimated consumption accounted for 75% of the recorded annual electricity consumption of the test area (the fourth objective). This result is acceptable, given the known limitations of the datasets and suggests that the method constitutes an improvement to stock energy modelling, thus meeting the overall aim. By inferring appliance electricity consumption and internal gains at a finer spatial resolution than previous methods, the diversity of energy consumption characteristics of the non-domestic stock may be represented more faithfully than by values applied to entire homogenised premises or premises types. The method may be used by policy makers as part of an urban energy model and as a means of evaluating potential energy interventions in the non-domestic stock, or parts thereof.
19
Guermat, Cherif. "Economic and time series analysis of electricity consumption in Algeria." Thesis, University of Exeter, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.303318.
Full text
20
DeMerchant, Elizabeth Ann. "User's Influence on Energy Consumption with Cooking Systems Using Electricity." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/29838.
Full textAbstract:
The research purpose was to explain the user's influence on energy consumption with cooking systems using electricity. This research was conducted in two phases. The research objective of Phase I was to determine if relationships exist that explain the user's influence (i.e., user characteristics--knowledge, experience, practices, and user interaction--and appliance operating time) on the energy consumption of cooking systems using electricity.
The ultimate aim of this research, the outcome of Phase II, was to identify categories of cooking style that explain the user's influence (i.e., user characteristics and appliance operating time) on energy consumption of cooking systems using electricity. The data used to answer the research question consisted of video tapes of consumers preparing the research menu, a survey, and data recorded on a data collection sheet by the researcher (i.e., watthour consumption). Simultaneous triangulation was used to answer the research questions. Phase I determined that energy consumption was correlated with knowledge, user interaction, practices, appliance operating time, cooking system interaction, goodness-of-fit, information, behavior, the user, and statistical interaction between the cooking system and goodness-of-fit. Independent variables explained 38.6% of the variation in energy consumption. However, when only the variables under the user's control were included in the regression model, just 25% of the variation in energy consumption was explained. Phase II determined the three most important factors that distinguish the five cooking style categories based on user characteristics (i.e., patient style, average style, uninformed style, hurried style, and hurried style with no control) were: (a) percentage of the sample that left the heat source on after cooking, (b) percentage of the sample that did not match the diameter of the heat source and the diameter of the cookware when using high heat, and (c) percentage of the sample that fried using high heat. Additional variables that differed among categories were: reusing hot elements, use of retained heat, and use of medium heat settings.
In summary, important factors in explaining variations in energy consumption include: inherent characteristics of the cooking system, user's knowledge, highest heat setting selected and matching the diameter of the heat source with the cookware diameter, leaving the heat source on after cooking, and selecting highest heat setting when frying. Three categories of consumers cooking style were developed (i.e., low, average, high energy consumption) to summarize the data. The highest heat setting selected and leaving the heat source on after cooking was completed were factors that distinguished consumers among the three categories.Ph. D.
21
Salter, Jennifer. "Understanding health changes through the analysis of electricity consumption data." Thesis, University of Sheffield, 2015. http://etheses.whiterose.ac.uk/13308/.
Full textAbstract:
With improvements in living standards and innovations in medical care, life expectancy has increased. However, although people are living longer, particularly in developed countries, they are not necessarily healthier during the additional years of life, with a rising number of people with long-term physical and mental health conditions that require supported living, for example, within a care home or hospital environment. In response to the rising economic costs of managing long term conditions, successive governments have developed policies to reduce the use of institutional environments (e.g., care homes) and of unplanned hospital admissions, and are encouraging the development of systems which aim to monitor, support and manage people’s health in their own home. These developments have lead to increased research on using remotely monitored, sensor-based technologies to provide relatives, carers and health care professionals with timely data about the well-being of older people living independently, and so provide timely and appropriate support effectively, thus helping them remain in their own homes, especially when they have long-term health problems. The aim of the research described in this thesis was to investigate the use of an electricity monitor to recognise and monitor changes in resident’s daily activities. This was achieved using two phases; the first conducted a survey to gather information about which activities and features that carers and relatives would like to have access to, so as to be reassured about their relative’s health and well being. The second phase collected and analysed electricity consumption data from four households for a one-week period, to develop models to identify when specific activities had been undertaken, e.g., using the shower, using a kettle. This research concluded that the monitoring of general and some specific activities is important to the relatives and carers, although the best form of reassurance about their relative’s situation was felt to be human contact. Following the analysis of the electricity consumption data, it was concluded that while it is possible to recognise appliance usage from whole house electricity consumption data, the variability and lack of transferability between houses and appliances would mean that the large-scale use of this type of monitoring would require considerable further development.
22
Chiu, Yuk Ha. "A cross-country empirical study on electricity demand /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?ECON%202004%20CHIU.
Full textAbstract:
Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2004.Includes bibliographical references (leaves 33-35). Also available in electronic version. Access restricted to campus users.
23
Overmaat, Eduard. "Balancing Contributions in the Nordic Electricity System : Who bears the brunt of electricity production and consumption patterns?" Thesis, Uppsala universitet, Elektricitetslära, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-393570.
Full textAbstract:
The share of intermittent weather-based renewable electricity sources has risen and will keep on rising in the Nordic electricity system, which will increase the need of balancing power in the Nordics. The previously developed concept of balancing contributions is used to look at the historic contribution of different power sources to the balancing on the grid. Three different time scales are taken into account: Daily variations, (bi-)weekly variations, and seasonal/yearly variations. This will aid in the understanding of the synergy of different sources on the grid, which, together with a deeper knowledge of the electricity market, might make it possible in the future to quantify the potential for balancing of sources within the Nordic grid. As a method to analyse the balancing contributions, a previously set-up online visualisation tool was used as an example, and this existing tool was revamped with a new software back-end using a database and automatic data collection. This allows one to be able to use a larger dataset, and for more functionality in the future, such as real-time updates and easier implementation of additional visualisations. Production and consumption data was gathered from Entso-e and SvK: the former has issues with data quality and the latter publishes data with a three-week delay which can only be obtained manually. The results from the previous research have been replicated, and a bigger dataset has been used to do the calculations, encompassing the years 2015-2018. The overall results show great similarity to that of the previous work. For the first time it was possible to plot the intrayear balancing contributions as a time series, which showed especially that the contributions of hydro power and electricity trade have changed over the period 2015-2018. There is a difference in hydro power balancing contributions based on geographical location, where Finnish hydro power is mainly a daily and—to a lesser extent—weekly regulator, Swedish hydro and especially Norwegian hydro have larger contributions on a yearly basis as well. There are even differences within countries, as the balancing contribution of hydro in bidding area SE2 has changed much more over time than hydro in SE1, for example. Other examples of interesting situations on the grid have also been highlighted using the online visualisation tool.
24
Agius, Joseph A. "Planned development of the Maltese electricity supply system." Thesis, Loughborough University, 1986. https://dspace.lboro.ac.uk/2134/32903.
Full textAbstract:
A planned development of the electricity supply system in the Maltese Islands is presented. The purpose of this project was to develop techniques for electricity supply system planning and design and to apply these techniques to a study of the development of the Maltese electricity supply system.
25
Mmatloa, Thaloki Gerald. "Management of electricity usage by household customers." Thesis, University of Limpopo (Turfloop Campus), 2010. http://hdl.handle.net/10386/609.
Full textAbstract:
Thesis (MBA) --University of Limpopo, 2010Management of electricity usage by household plays an important role in the growth of the country’s economy, and the avoidance of load shedding from Eskom. Electricity usage is very important for the growth of the economy and creation of job opportunities. The management of electricity usage by household’s customers will play a very critical role in the growth of the country’s economy and the creation of jobs. The contribution of households in applying the electricity saving techniques will reduce the risk of load shedding from Eskom during summer and winter. The save usage of electricity will give Eskom enough chance to build power stations to keep up with the demands and the growth of the South African economy. For the household customers to contribute positively, Eskom and the municipalities should conduct road shows to educate customers about the save usage of electrical appliances and the saving techniques that can be applied by households. Customer awareness campaigns should be conducted in both rural, urban and semi-urban areas. It will be very important for Eskom to communicate with the municipalities to run the awareness campaigns in the urban areas due to the high demand of electricity by households coming from the urban areas. The majority of households who are using the high consuming appliances of electricity reside in the urban areas and can play a vital role in minimizing the risk of load shedding that affected the country negatively in 2008. The quantitative research method was followed for this research. A questionnaire was used to collect the data from the household’s participants. Forty households from the five areas of Polokwane took part in the research totalling 200 participants. It was discovered that the customer awareness campaigns were conducted by Eskom in the areas where they service customers, although there are some gaps in other areas where the customers are complaining about lack of road shows to teach households about the electricity saving tips. Municipalities in all the five areas of Polokwane where the research was conducted are still lagging behind with the customer’s awareness campaigns. However Eskom customer services and the municipalities can work together and conduct road shows to reach more customers in order to reduce the risk of load shedding and power interruptions.
26
Segtnan, Ida Lund. "Assessing the Environmental Costs and Benefits of Households Electricity Consumption Management." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for elkraftteknikk, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-13686.
Full textAbstract:
In this study the environmental costs and benefits of smart metering technology systems installed in households in Norway have been assessed. Smart metering technology systems enable mechanisms to manage electricity consumption by shifting loads. With the use of Life Cycle Assessment (LCA) and the ReCiPe method for impact assessment, the life cycle impacts of installation and operation of a system in a household have been found. Environmental benefits of using the systems to manage electricity consumption have been quantified. The results of the study indicated that the environmental costs of smart metering technology systems mainly are caused by the production of system components and system electricity use during operation. For the production of system components, the use of electronics in the components was generally the major contributor to the total environmental impacts. Further, the systems metal depletion potential was high relative to other environmental impacts after normalization in impact assessment. The main environmental benefits of smart metering technology systems in a Norwegian perspective will be in a critical supply situation of electricity to avoid use of reserve capacity gas power plants, and the results from the study showed that the systems in such a case can contribute to an avoided emission of greenhouse gases. Load shifting from a general basis may however not always have environmental benefits and this will depend on the existing alternatives for electricity production.
27
Johansson, Andreas. "Fault Detection of Hourly Measurements in District Heat and Electricity Consumption." Thesis, Linköping University, Department of Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2804.
Full textAbstract:
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.
28
Hammarberg, Filip. "Visualization of electricity consumption and solar panel production for house owners." Thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-148960.
Full textAbstract:
In only two hours, the earth receives sunlight containing the amount of energy equivalent to one year’s electricity consumption of the entire population. Yet only 0.09 percent of the electricity production in Sweden 2016 came from solar energy. To increase that amount, the energy and communications company Umeå Energi offers solar panels to their customers. While doing that, they have identified a desire for their customers to monitor their electricity consumption and production at the same place – something that the service lacks today. This thesis investigates the actual needs behind why the solar customers want to know the electricity consumption and production information, in what contexts it will be accessed, and how that can be visualized to meet their needs. Starting with a literature study to understand the area, the work continues by meeting and interviewing customers to collect insights. These insights are then used as a foundation when creating and testing prototypes repeatedly together with the customers. It was found that being self-sufficient is one of the most important reasons for their solar panel customers for producing their own electricity. The prototypes that were created aimed to address that self-sufficiency need, together with visual representations of their electricity production and consumption – both historically as well as with live data.
29
Gasealahwe, Boitumelo. "Relationship between electricity prices, consumption and economic growth in South Africa." Master's thesis, Faculty of Commerce, 2021. http://hdl.handle.net/11427/32701.
Full textAbstract:
This study analyses the relationship between electricity prices, consumption and economic growth at national and per sector levels in South Africa over the period from 2006 to 2017 using the auto-regressive distributed lag (ARDL) bounds testing approach and error correction model (ECM). With regards to electricity consumption, in the mining and residential sectors, the relationship between electricity consumption and GDP is insignificant and thus adheres to the neutrality hypothesis. In contrast, in the services, transportation and industrial sectors, there is a positive relationship between GDP and electricity consumption, which adheres to the conservative hypothesis. Lastly, the agricultural sector has a positive relationship between electricity consumption and economic growth in the short run, and thus adheres to the growth hypothesis. In the case of electricity prices and electricity consumption, the results find that the relationship is insignificant on a national basis and this is true for the services, transport, residential and agricultural sectors too, whereas there is a negative association with electricity consumption in the mining sector while the industrial sector has a negative association with electricity prices. The results for the relationship between electricity prices and electricity consumption show that in the national, services sector, transport sector, residential and agricultural sectors, electricity consumption has an insignificant relationship with the electricity prices. This is in contrast to the mining sector, whose electricity consumption is negatively associated with electricity prices while the industrial sector electricity consumption has a positive and significant relationship with electricity prices. With regards to the relationship between electricity prices and GDP, the results find that there is an elastic association in the national, services, mining, and industrial sectors with a negative impact on the GDP in the long run. In contrast, the relationship between electricity prices and GDP in the transport and residential sectors is insignificant.
30
Cox, Samuel Stephen. "Predicting electricity consumption and cost for South African mines / S.S. (Stephen) Cox." Thesis, North-West University, 2013. http://hdl.handle.net/10394/9640.
Full textAbstract:
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.
31
Contreras, Sergio. "Regional electricity demand in the United States." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2008. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.
Full text
32
Koliesnichenko, Anastasiia, and Анастасія Сергіївна Колєсніченко. "The key trends transforming the electricity market: opportunities and challenges." Thesis, Херсонський національний технічний університет, 2019. http://repository.kpi.kharkov.ua/handle/KhPI-Press/41772.
Full textAbstract:
Significant changes in energy prices and production increase. At the same time, the slowdown in economic growth in emerging market countries is occurring, along with geopolitical changes and instability, which effectively dragged energy demand and supply. In that case it is necessary to use such tools as confidently planning for transformative enhancements in productivity, reliability, safety, customer experience, compliance, and revenue management. The result dimensions of these exciting opportunities can be demonstrated in three developmental directions based on productivity and efficiency, the customer experience, and new frontiers.
33
Si, Yau-li. "Forecasts of electricity demand and their implication for energy developments in Hong Kong." [Hong Kong : University of Hong Kong], 1990. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13009102.
Full text
34
Nedzingahe, Livhuwani. "Forecasting models for operational and tactical requirements in electricity consumption: The case of the Ferrochrome Sector in South Africa." Thesis, University of Limpopo (Medunsa Campus ), 2010. http://hdl.handle.net/10386/1150.
Full textAbstract:
Thesis (Mathematics) -- University of Limpopo, 2010Forecasting 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
35
Liu, Ying. "Multi-objective optimisation methods for minimising total weighted tardiness, electricity consumption and electricity cost in job shops through scheduling." Thesis, University of Nottingham, 2014. http://eprints.nottingham.ac.uk/14125/.
Full textAbstract:
Manufacturing enterprises nowadays face the challenge of increasing energy prices and requirements to reduce their emissions. Most reported work on reducing manufacturing energy consumption focuses on the need to improve the efficiency of resources (machines). The potential for energy reducing at the system-level has been largely ignored. At this level, operational research methods can be employed as the energy saving approach. The advantage is clearly that the scheduling and planning approach can be applied across existing legacy systems and does not require a large investment. For the emission reduction purpose, some electricity usage control policies and tariffs (EPTs) have been promulgated by many governments. The Rolling Blackout policy in China is one of the typical EPTs, which means the government electricity will be cut off several days in every week for a specific manufacturing enterprise. The application of the Rolling Blackout policy results in increasing the manufacturing enterprises’ costs since they choose to start to use much more expensive private electricity to maintain their production. Therefore, this thesis develops operational research methods for the minimisation of electricity consumption and the electricity cost of job shop type of manufacturing systems. The job shop is selected as the research environment for the following reasons. From the academic perspective, energy consumption and energy cost reduction have not been well investigated in the multi-objective scheduling approaches to a typical job shop type of manufacturing system. Most of the current energy-conscious scheduling research is focused on single machine, parallel machine and flow shop environments. From the practical perspective, job shops are widely used in the manufacturing industry, especially in the small and medium enterprises (SMEs). Thus, the innovative electricity-conscious scheduling techniques delivered in this research can provide for plant managers a new way to achieve cost reduction. In this thesis, mathematical models are proposed for two multi-objective job shop scheduling optimisation problems. One of the problems is a bi-objective problem with one objective to minimise the total electricity consumption and the other to minimise the total weighted tardiness (the ECT problem). The other problem is a tri-objective problem which considers reducing total electricity consumption, total electricity cost and total weighted tardiness in a job shop when the Rolling Blackout policy is applied (the EC2T problem). Meta-heuristics are developed to approximate the Pareto front for ECT job shop scheduling problem including NSGA-II and a new Multi-objective Genetic Algorithm (GAEJP) based on the NSGA-II. A new heuristic is proposed to adjust scheduling plans when the Rolling Blackout policy is applied, and to help to understand how the policy will influence the performance of existing scheduling plans. NSGA-II is applied to solve the EC2T problem. Six scenarios have been proposed to prove the effectiveness of the aforementioned algorithms. The performance of all the aforementioned heuristics have been tested on Fisher and Thompson 10×10, Lawrence 15×10, 20×10 and 15×15 job shop scenarios which were extended to incorporate electrical consumption profiles for the machine tools. Based on the tests and comparison experiments, it has been found that by applying NSGA-II, the total non-processing electricity consumption in a job shop can decrease considerably at the expense of the schedules’ performance on the total weighted tardiness objective when there are tight due dates for jobs. When the due dates become less tight, the sacrifice of the total weighted tardiness becomes much smaller. By comparing the Pareto fronts obtained by GAEJP and by NSGA-II, it can be observed that GAEJP is more effective in reducing the total non-processing electricity consumption than NSGA-II, while not necessarily sacrificing its performance on total weighted tardiness. Thus, the superiority of the GAEJP in solving the ECT problem has been demonstrated. The scheduling plan adjustment heuristic has been proved to be effective in reducing the total weighted tardiness when the Rolling Blackout policy is applied. Finally, NSGA-II is proved to be effective to generate compromised scheduling plans for using the private electricity. This can help to realise the trade-off between the total weighted tardiness and the total electricity cost. Finally, the effectiveness of GAJEP in reducing the total non-processing electricity consumption has been validated in a real-world job shop case.
36
Dodds, Gordon Ivan. "Modelling and forecasting electricity demand using aggregate and disaggregate data." Thesis, Queen's University Belfast, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.306073.
Full text
37
Bartusch, Cajsa. "Boosting behavioral change in residential electricity consumption : demand response programs and feedback." Doctoral thesis, Mälardalens högskola, Akademin för hållbar samhälls- och teknikutveckling, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-13214.
Full textAbstract:
As part of realizing national and European climate ambitions, it is imperative to bring about increased energy efficiency and consumption flexibility in the residential sector of the Swedish power market. In addition to governmental policy instruments to this end, market-based measures play an important role in making behavioral change in domestic electricity use happen. In light of the prevailing lack of incentives for residential consumers to save electricity and cut peak demand at times of physical and financial market constraints, the research studies that form the basis of this thesis have the aim of adding to the body of knowledge on policy instruments for the purpose of boosting behavioral change in residential electricity consumption. The research has accordingly contributed to the general statistics on residential electricity consumption, which constitute the starting point for policy instrument development, and augmented knowledge on the merits of residential demand response programs involving hourly settlements in power trading and demand-based, time-of-use tariffs in power distribution as well as graphic feedback on individual households’ electricity use by means of a statistics service provided over the Internet. The overall results have shown that household behavior, together with physical factors such as heating systems, help explain the sizeable differences in electricity consumption among homeowners. Statistical analysis of variance has in this context proven to be an effective method for identifying key indicators of policy development. Power suppliers and electricity consumers as well as society as a whole have been found to gain substantially from hourly settlements in retail. To suppliers, the greatest benefits are associated with risk management, while the major advantage to customers is that they are provided with an opportunity to reduce their electricity costs. It has also been empirically demonstrated that electricity users are willing to adjust their consumption to a demand-based, time-varying distribution tariff. Households generally have a favorable attitude towards this type of distribution tariff, seeing as they indirectly have a positive impact on the environment. Providing households with feedback over the Internet on their individual electricity use and demand has been shown to contribute to an increased awareness and lead to energy efficiency in homes. Easy accessibility and simplicity have proven to be key success factors in this context. Combining conventional bar charts, color symbolism and historic feedback is expedient in this respect.Att åstadkomma ökad energieffektivisering och förbrukningsflexibilitet i den svenska elmarknadens bostadssektor är ett nödvändigt led i realiseringen av de nationella och europeiska klimatmålen. Utöver statliga styrmedel för detta ändamål spelar även marknadsbaserade åtgärder en viktig roll för att få till stånd beteendeförändringar i samband med hushålls elanvändning. Mot bakgrund av den rådande bristen på bevekelsegrunder för elkonsumenter att spara el och minska effektuttaget när marknadens fysiska och finansiella begränsningar ger sig till känna har det övergripande syftet med forskarstudierna som ligger till grund för den här avhandlingen varit att bidra till den samlade kunskapen om styrmedel som främjar beteendeförändringar i hushålls elanvändning. Forskningen har således bidragit till den generella statistiken om elanvändning i bostäder, som utgör utgångspunkten för utvecklingen av styrmedel, samt ökat kunskapen om vinsterna med timavräkning inom elhandeln, effektbaserade tidstariffer inom eldistributionen i bostadssektorn och grafisk återkoppling på enskilda hushålls elanvändning med hjälp av en statistiktjänst som tillhandahålls via Internet. De övergripande resultaten har visat att hushålls beteende, jämte fysiska faktorer såsom uppvärmningssystem, bidrar till att förklara de stora skillnaderna i elkonsumtionen bland villaägare. Statistisk variansanalys har i det sammanhanget visat sig vara en ändamålsenlig metod för att identifiera viktiga indikatorer för utveckling av styrmedel. Såväl enskilda elleverantörer och elkonsumenter som samhället i stort har visat sig ha mycket att vinna på timavräkning inom elhandeln. För elhandlaren är de största fördelarna förknippade med riskhantering, medan vinsten för kunderna består i ökade möjligheter att minska sina elkostnader. Det har även empiriskt påvisats att elkonsumenter är beredda att anpassa sin förbrukning till en effektbaserad tidstariff. Hushåll har generellt en välvillig inställning till den här typen av tariffer, eftersom de indirekt har en positiv effekt på miljön. Att tillhandahålla hushåll med återkoppling avseende deras individuella elanvändning och effektuttag via Internet har visat sig bidra till en ökad medvetenhet och leda till energieffektiviseringar i bostäder. Lättillgänglighet och enkelhet har i det sammanhanget visat sig vara centrala framgångsfaktorer. Att kombinera konventionella stapeldiagram, färgsymbolik och historisk återkoppling är ändamålsenligt i det avseendet.
38
Kaufman, Joshua Daniel 1978. "SeeGreen : a tool for real-time distributed monitoring of home electricity consumption." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/8569.
Full textAbstract:
Thesis (M.Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.Includes bibliographical references (p. 51).
This thesis presents SeeGreen, a system designed to enable easy collection of distributed information about power usage (quantity and quality) in residential facilities. In Europe many appliances are emerging that incorporate power measuring devices for power regulation. However, products for retrofitting old appliances or recording measurements from other power consuming devices are not available. Due to aging of appliances, varying consumer habits and appliance standby modes, estimating the power consumption behavior of a device is remarkably difficult. Products in "off' (or standby) modes consume an estimated ten percent of the electrical energy used in a house. With built in AC switches, SeeGreen has the capacity to minimize or even eliminate this unnecessary waste. Perhaps the largest downfall of modem monitoring systems is a lack of real-time (immediate) feedback about an appliance's consumption. Feedback of this nature highly encourages change in consumer behavior. SeeGreen facilitates reduced electricity consumption by providing information to consumers and automatic control devices.
by Joshua Daniel Kaufman.
M.Eng.and S.B.
39
Sten, Amanda, and Katja Åström. "Opportunities and barriers for an increased flexibility in residential consumers’ electricity consumption." Thesis, KTH, Energi och klimatstudier, ECS, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191446.
Full textAbstract:
I Sverige står hushållen för en stor del av den slutliga elanvändningen och deras konsumtionsmönster bidrar till att skapa höga förbrukningstoppar, särskilt under vintermånaderna när elbehovet är som störst. Om hushållen kunde tänka sig att vara mer flexibla i när de använder el skulle förbrukningstoppar kunna dämpas avsevärt och balansen mellan elproduktion och elanvändning bli lättare att upprätthålla. Idag utnyttjas inte efterfrågeflexibilitet i någon större utsträckning, förutom den från vissa elintensiva industrier. Den flexibla kapacitet enskilda hushåll skulle kunna bidra med är naturligtvis lägre än hos industrier, men sammanslaget skulle hushållskunders flexibilitet kunna ge en substantiell inverkan på elsystemet. Vid låga utetemperaturer finns det en uppskattad potential att genomföra effektjusteringar om cirka 1 400 – 3 100 MW om värmelasten hos drygt hälften av samtliga eluppvärmda hus i Sverige omdisponeras till andra tidpunkter, och ytterligare några hundra MW om drygt hälften av samtliga hushåll i Sverige vore flexibla i när de använder hushållsel. Enligt en studie av Broberg m.fl. (2016) skulle drygt hälften av hushållen i Sverige kunna tänka sig att vara flexibla, beroende på vad flexibel innebär. Hushåll som använder el för uppvärmning kan vara flexibla genom att tillfälligt öka eller minska inomhustemperaturen, eller om de använder el i kombination med något annat uppvärmningssätt – genom att byta energikälla. Justeringen kan även ske automatiskt om uppvärmningssystemet är utrustat med central styrutrustning. Om ett stort antal kunders flexibla laster samlas ihop av en marknadsaktör skulle den totala flexibla lasten kunna säljas som kapacitet på grossistmarknaden för el eller erbjudas som upp- eller nedregleringsbud på reglermarknaden. Studien av Broberg m.fl. (2014) har även analyserat hur stor ekonomisk kompensation hushåll vill ha i utbyte mot att vara flexibla. Sett till den flexibla kapacitet hushållskunders efterfrågeflexibilitet bedöms motsvara, cirka 1 400 – 3 100 MW, är kompensationskraven legitimerade, åtminstone om den flexibla kapaciteten erbjuds på någon marknadsplats för elhandel. Styrtjänster som innebär att elanvändningen automatiskt optimeras efter elpriset kan dock vara dyra idag, vilket innebär att det främst tros vara hushåll med hög elförbrukning som utnyttjar dem och de bör därför subventioneras. En annan form av flexibilitet är att anpassa elanvändningen efter det timvarierande elhandelspriset. Den enda förutsättningen för att konsumenten ska tjäna på en sådan anpassning är att elförbrukningen mäts och debiteras på timbasis, vilket är fallet för de relativt få kunder som har valt att teckna timprisavtal. På grund av att konsumentpriset på el inte varierar särskilt mycket saknas incitament för att kunder ska vilja anpassa sin användning efter priset. Det behövs därför en mer effektiv prissättning som exempelvis förstärker volatiliteten eller gör det dyrare eller billigare att använda el vid vissa tidpunkter.In Sweden, residential consumers account for a large share of the final electricity consumption. Their consumption patterns pose great impact on the network power peaks, especially during the winter. If residential consumers were more flexible in their consumption, peaks would be alleviated considerably and the balance between electricity supply and demand would more easily be maintained. Today, demand side flexibility is not utilized to any greater extent, except the one from energy intensive industries. De flexible capacity a single household could contribute with is of course less than within industries, but if flexible capacity from a large number of households were bundled up it would provide a considerable impact on the electricity system. At low outdoor temperatures there is an estimated potential to reach power adjustments in the size of 1 400 – 3 100 MW if the heat load in just over half of the electric heated houses in Sweden were displaced, and a few hundred more if residential consumers were flexible in their consumption of domestic electricity. According to a study by Broberg et al (2016) approximately half the population would consider to be flexible in their electricity consumption under the right circumstances. Households that use electricity for heating can be flexible through temporarily adjust the indoor temperature, or – if they heat their homes with electricity in combination with another heat source – by switching heat source. The adjustment can also be automatic if the heating system is equipped with a central control unit. If flexible capacity from a large number of households is bundled up into grid worthy demand response by a market actor, the capacity could be offered as bids on organized electricity markets. The study by Broberg et al (2014) also analysed how much compensation households require in exchange for being flexible. The compensation levels are justified with regard to the flexible capacity that can be gathered form households, 1 400 – 3 100 MW, at least as long as the capacity is sold in an organized electricity market. Services for automatic control of heating systems, where the power output is optimized after the varying electricity price, can be expensive today, which indicates that mainly households with a high electricity consumption utilize them today. Hence, they need to be subsidized. Demand side flexibility can also be to manually change consumption patterns in response to price signals. The only precondition is that the electricity consumption is measured and billed on an hourly basis, which is the case for the relatively few consumers with hourly rate agreements. The volatility of the electricity price is however subdued due to the large share of fixed surcharges, which means there is lack of incentive for consumers to adapt their consumption in response to price variations. Hence, the volatility needs to be amplified through efficient pricing.
40
van, der Meer Dennis. "Spatio-temporal probabilistic forecasting of solar power, electricity consumption and net load." Licentiate thesis, Uppsala universitet, Fasta tillståndets fysik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-363448.
Full textAbstract:
The increasing penetration of renewable energy sources into the electricity generating mix poses challenges to the operational performance of the power system. Similarly, the push for energy efficiency and demand response—i.e., when electricity consumers are encouraged to alter their demand depending by means of a price signal—introduces variability on the consumption side as well. Forecasting is generally viewed as a cost-efficient method to mitigate the adverse effects of the aforementioned energy transition because it enables a grid operator to reduce the operational risk by, e.g., unit-commitment or curtailment. However, deterministic—or point—forecasting is currently still the norm. This thesis focuses on probabilistic forecasting, a method with which the uncertainty ac- companying the forecast is expressed by means of a probability distribution. In this framework, the thesis contributes to the current state-of-the-art by investigating properties of probabilistic forecasts of PV power production, electricity consumption and net load at the residential and distribution level of the electricity grid. The thesis starts with an introduction to probabilistic forecasting in general and two models in specific: Gaussian processes and quantile regression. The former model has been used to produce probabilistic forecasts of PV power production, electricity consumption and net load of individual residential buildings—particularly challenging due to the stochasticity involved— but important for home energy management systems and potential peer-to-peer energy trading. Furthermore, both models have been utilized to investigate what effects spatial aggregation and increasing penetration have on the predictive distribution. The results indicated that only 20- 25 customers—out of a data set containing 300 customers—need to be aggregated in order to improve the reliability of the probabilistic forecasts. Finally, this thesis explores the potential of Gaussian process ensembles, which is an effective way to improve the accuracy of the forecasts.
41
Bragg, Albert A. Jr. "A Journey Toward Sustainable Behavior: A Project to Stimulate Reduced Electricity Consumption." Cleveland State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=csu1594057737376892.
Full text
42
Leong, Kuan Long. "Detecting anomalies in activity patterns of lone occupants from electricity consumption data." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/56586.
Full textAbstract:
As the global population is ageing, the demand for elderly care facilities and services is expected to increase. Assisted living technologies for detecting medical emergencies and assessing the wellness of the elderly are becoming more popular. A person normally performs activities of daily living (ADLs) on a regular basis. A person who is able to perform recurring ADLs indicates a certain wellness level. Anomalies in activity patterns of a person might indicate changes in the person's wellness. A method is proposed in this thesis for detecting anomalies in activity patterns of a lone occupant using electricity consumption measurements of his/her residence. The proposed method infers anomalies in activity patterns of an occupant from electricity consumption patterns without a need of explicitly monitoring the underlying individual activities. The proposed method provides a score which is a quantitative assessment of anomalies in the electricity consumption pattern of an occupant for a given day. A survey was conducted to obtain the hourly activities of three lone occupants for a month. The level of suspicion values, which are quantitative assessments of anomalies in the daily activity patterns of the occupants, were deduced from the survey. Using Fuzzy C-Means (FCM) clustering with Euclidean distance measure, the scores and level of suspicion values were clustered respectively. A day was then classified as regular or irregular based on the clustering results of the scores and level of suspicion values respectively. The results showed that anomalies in electricity consumption patterns can effectively reflect anomalies in the underlying activity patterns. The results also showed that the proposed feature and model based method outperforms a chosen raw data based approach. The performance of the proposed method was improved when subsets of features were considered based on the minimum Redundancy Maximum Relevance (mRMR) feature selection. A supervised learning method based on the Curious Extreme Learning Machine (C-ELM) was then proposed. The proposed method based on C-ELM (PM-CELM) outperforms the proposed method based on FCM (PM-FCM), but PM-FCM can operate without labelled training data.Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
43
Linton, Thomas. "Forecasting hourly electricity consumption for sets of households using machine learning algorithms." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186592.
Full textAbstract:
To address inefficiency, waste, and the negative consequences of electricity generation, companies and government entities are looking to behavioural change among residential consumers. To drive behavioural change, consumers need better feedback about their electricity consumption. A monthly or quarterly bill provides the consumer with almost no useful information about the relationship between their behaviours and their electricity consumption. Smart meters are now widely dispersed in developed countries and they are capable of providing electricity consumption readings at an hourly resolution, but this data is mostly used as a basis for billing and not as a tool to assist the consumer in reducing their consumption. One component required to deliver innovative feedback mechanisms is the capability to forecast hourly electricity consumption at the household scale. The work presented by this thesis is an evaluation of the effectiveness of a selection of kernel based machine learning methods at forecasting the hourly aggregate electricity consumption for different sized sets of households. The work of this thesis demonstrates that k-Nearest Neighbour Regression and Gaussian process Regression are the most accurate methods within the constraints of the problem considered. In addition to accuracy, the advantages and disadvantages of each machine learning method are evaluated, and a simple comparison of each algorithms computational performance is made.För att ta itu med ineffektivitet, avfall, och de negativa konsekvenserna av elproduktion så vill företag och myndigheter se beteendeförändringar bland hushållskonsumenter. För att skapa beteendeförändringar så behöver konsumenterna bättre återkoppling när det gäller deras elförbrukning. Den nuvarande återkopplingen i en månads- eller kvartalsfaktura ger konsumenten nästan ingen användbar information om hur deras beteenden relaterar till deras konsumtion. Smarta mätare finns nu överallt i de utvecklade länderna och de kan ge en mängd information om bostäders konsumtion, men denna data används främst som underlag för fakturering och inte som ett verktyg för att hjälpa konsumenterna att minska sin konsumtion. En komponent som krävs för att leverera innovativa återkopplingsmekanismer är förmågan att förutse elförbrukningen på hushållsskala. Arbetet som presenteras i denna avhandling är en utvärdering av noggrannheten hos ett urval av kärnbaserad maskininlärningsmetoder för att förutse den sammanlagda förbrukningen för olika stora uppsättningar av hushåll. Arbetet i denna avhandling visar att "k-Nearest Neighbour Regression" och "Gaussian Process Regression" är de mest exakta metoder inom problemets begränsningar. Förutom noggrannhet, så görs en utvärdering av fördelar, nackdelar och prestanda hos varje maskininlärningsmetod.
44
Kvalheim, Miles R. "Residental Electricity Demand: An Analysis of the Current and Future United States Electricity Grid and Its Impact on Power Consumption." Scholarship @ Claremont, 2012. http://scholarship.claremont.edu/cmc_theses/352.
Full textAbstract:
The nature of electrical power requires specific infrastructure in order to operate adequately. Currently, the United States electricity grid contains a number of bottlenecking inefficiencies that arise from the aging infrastructure of the system. This paper examines the current state of the United States electricity grid, how potential changes in weather variables can affect the electricity consumption of residential consumers, and how implementation of Smart Grid technology can potentially mitigate these issues. It is determined through regression analysis that each weather variable that was tested proves significant for at least one of the consumers compared. This indicates that there is an enormous magnitude of individual variables that factor into residential electricity consumption and that more efficient and integrated electricity practices are necessary to optimize efficiency.
45
Nguyen, Duy Huu Manh. "Analysing electricity markets with evolutionary computation." University of Western Australia. School of Electrical, Electronic and Computer Engineering, 2002. http://theses.library.uwa.edu.au/adt-WU2003.0018.
Full textAbstract:
The use of electricity in 21st century living has been firmly established throughout most of the world, correspondingly the infrastructure for production and delivery of electricity to consumers has matured and stabilised. However, due to recent technical and environmental–political developments, the electricity infrastructure worldwide is undergoing major restructuring. The forces driving this reorganisation are a complex interplay of technical, environmental, economic and political factors. The general trend of the reorganisation is a dis–aggregation of the previously integrated functions of generation, transmission and distribution, together with the establishment of competitive markets, primarily in generation, to replace previous regulated monopolistic utilities. To ensure reliable and cost effective electricity supply to consumers it is necessary to have an accurate picture of the expected generation in terms of the spatial and temporal distribution of prices and volumes. Previously this information was obtained by the regulated utility using technical studies such as centrally planned unit–commitment and economic–dispatch. However, in the new deregulated market environment such studies have diminished applicability and limited accuracy since generation assets are generally autonomous and subject to market forces. With generation outcomes governed by market mechanisms, to have an accurate picture of expected generation in the new electricity supply industry, it is necessary to complement traditional studies with new studies of market equilibrium and stability. Models and solution methods have been developed and refined for many markets, however they cannot be directly applied to the generation market due to the unique nature of electricity, having high inelastic demand, low storage capability and distinct transportation requirements. Intensive effort is underway to formulate solutions and models that specifically reflect the unique characteristics of the generation market. Various models have been proposed including game theory, stochastic and agent–based systems. Similarly there is a diverse range of solution methods including, Monte–Carlo simulations, linear–complimentary and quadratic programming. These approaches have varying degrees of generality, robustness and accuracy, some being better in certain aspects but weaker in others. This thesis formulates a new general model for the generation market based on the Cournot game, it makes no conjectures about producers’ behaviour and assumes that all electricity produced is immediately consumed. The new formulation characterises producers purely by their cost curves, which is only required to be piece–wise differentiable, and allows consumers’ characteristics to remain unspecified. The formulation can determine dynamic equilibrium and multiple equilibria of markets with single and multiple consumers and producers. Additionally stability concepts for the new market equilibrium is also developed to provide discrimination for dynamic equilibrium and to enable the structural stability of the market to be assessed. Solutions of the new formulation are evaluated by the use of evolutionary computation, which is a guided stochastic search paradigm that mimics the operation of biological evolution to iteratively produce a population of solutions. Evolutionary computation is employed as it is adept at finding multiple solutions for underconstrained systems, such as that of the new market formulation. Various enhancements to significantly improve the performance of the algorithms and simplify its application are developed. The concept of convergence potential of a population is introduced together with a system for the controlled extraction of such potential to accelerate the algorithm’s convergence and improve its accuracy and robustness. A new constraint handling technique for linear constraints that preserves the solution’s diversity is also presented together with a coevolutionary solution method for the multiple consumers and producers market. To illustrate the new electricity market formulation and its evolutionary computation solution methods, the equilibrium and stability of a test market with one consumer and thirteen thermal generators with valve point losses is examined. The case of a multiple consumer market is not simulated, though the formulation and solution methods for this case is included. The market solutions obtained not only confirms previous findings thus validating the new approach, but also includes new results yet to be verified by future studies. Techniques for market designers, regulators and other system planners in utilising the new market solutions are also given. In summary, the market formulation and solution method developed shows great promise in determining expected generation in a deregulated environment.
46
Meibodi, Ali. "Efficiency considerations in the electricity supply industry : the case of Iran." Thesis, University of Surrey, 1998. http://epubs.surrey.ac.uk/804387/.
Full text
47
McCafferty, Peter. "Forecasting electricity demand in the industrial sector based on disaggregate data." Thesis, Queen's University Belfast, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.385049.
Full text
48
Gasteen, M. R. "Propagation of mains marked control signals on an electricity distribution network." Thesis, University of Bristol, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.354449.
Full text
49
Monteforte, Raul. "Energy and styles of development : the case of electricity in Mexico." Thesis, University of Sussex, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.278249.
Full text
50
Nyulu, Thandekile. "Weather neutral models for short-term electricity demand forecasting." Thesis, Nelson Mandela Metropolitan University, 2013. http://hdl.handle.net/10948/d1018751.
Full textAbstract:
Energy demand forecasting, and specifically electricity demand forecasting, is a fun-damental feature in both industry and research. Forecasting techniques assist all electricity market participants in accurate planning, selling and purchasing decisions and strategies. Generation and distribution of electricity require appropriate, precise and accurate forecasting methods. Also accurate forecasting models assist producers, researchers and economists to make proper and beneficial future decisions. There are several research papers, which investigate this fundamental aspect and attempt var-ious statistical techniques. Although weather and economic effects have significant influences on electricity demand, in this study they are purposely eliminated from investigation. This research considers calendar-related effects such as months of the year, weekdays and holidays (that is, public holidays, the day before a public holiday, the day after a public holiday, school holidays, university holidays, Easter holidays and major religious holidays) and includes university exams, general election days, day after elections, and municipal elections in the analysis. Regression analysis, cate-gorical regression and auto-regression are used to illustrate the relationships between response variable and explanatory variables. The main objective of the investigation was to build forecasting models based on this calendar data only and to observe how accurate the models can be without taking into account weather effects and economic effects, hence weather neutral models. Weather and economic factors have to be forecasted, and these forecasts are not so accurate and calendar events are known for sure (error-free). Collecting data for weather and economic factors is costly and time consuming, while obtaining calendar data is relatively easy.