Thesis (D. Tech. (Engineering: Electric)) -- Central University of technology, 2012 / Because power generation relies heavily on electricity demand, consumers are required to wisely manage their loads to consolidate the power utility‟s optimal power generation efforts. Consequently, accurate and reliable electric load forecasting systems are required. Prior to the present situation, there were various forecasting models developed primarily for electric load forecasting. Modelling short term load forecasting using artificial neural networks has recently been proposed by researchers. This project developed a model for short term load forecasting using a neural network. The concept was tested by evaluating the forecasting potential of the basic feedforward and the cascade forward neural network models. The test results showed that the cascade forward model is more efficient for this forecasting investigation. The final model is intended to be a basis for a real forecasting application. The neural model was tested using actual load data of the Bloemfontein reticulation network to predict its load for half an hour in advance. The cascade forward network demonstrates a mean absolute percentage error of less than 5% when tested using four years of utility data. In addition to reporting the summary statistics of the mean absolute percentage error, an alternate method using correlation coefficients for presenting load forecasting performance results are shown. This research proposes that a 6:1:1 cascade forward neural network can be trained with data from a month of a year and forecast the load for the same month of the following year. This research presents a new time series modeling for short term load forecasting, which can model the forecast of the half-hourly loads of weekdays, as well as of weekends and public holidays. Obtained results from extensive testing on the Bloemfontein power system network confirm the validity of the developed forecasting approach. This model can be implemented for on-line testing application to adopt a final view of its usefulness.

Thesis (MPhil)--Stellenbosch University, 2008. / Towards the end of 2007, South Africa started experiencing widespread rolling electricity blackouts as the electricity demand exceeded the supply from energy giant Eskom. The crisis reached its tipping point when industries, including Sasol, were requested to reduce their electricity consumption by 10%. The purpose of this research was to identify alternative futures for Sasol in the light of the current electricity crisis. The scenario process was used to develop the following independent scenarios for Sasol: • Fuel to the fire. The country is amidst an ongoing nationwide electricity crisis and Sasol is still dependent on Eskom for more than 50% of its electricity demand. The end result is reduced turnover, shortage of liquid fuels and a decrease in Sasol’s contribution to the economy. • Ignorance is bliss. This is a world where Sasol is independent of Eskom for electricity supply despite the country’s continuing electricity crisis. Independence is ideal but unfortunately it comes at a cost. It is about taking painful action in the near term to forestall even more painful consequences in the future. • Blessing in disguise. Sasol is dependent on Eskom for the majority of its electricity requirement. The reliability of electricity supply in South Africa recovered and there is an overall awareness regarding energy efficiency and a positive adoption of alternative energy technologies. • Icing on the cake. Sasol is completely independent of Eskom and Eskom managed to restore the integrity of electricity supply. The end result, Sasol can continue with its planned growth and expansion. In order for the scenarios to be useful for Sasol, it is necessary to incorporate them into the strategic agenda. Some considerations include the gradual replacement of traditional fossil fuels, carbon capture and sequestration, advanced coal electricity generation (clean coal technology), increasing the use of renewable energy sources and developing the hydrogen economy.

[pt] Nos últimos anos, o custo com energia elétrica tem aumentado de forma significativa para os consumidores no Brasil. Grandes consumidores, como indústrias e comércios, atualmente dispõem de alternativas para mitigar estes custos, como a otimização do contrato de demanda, a correção do baixo fator de potência, a utilização de geração própria, renovável ou não renovável, além da possibilidade de migrar para o mercado livre de energia elétrica, com diversas modalidades de contratos, preços e prazos. Já os consumidores residenciais, em função dos custos menores com as faturas de energia e da limitação técnica dos medidores, até agora dispunham de poucos mecanismos para atenuar seus custos. Entretanto, nos últimos anos tem sido cada vez mais comum a utilização de geração distribuída, principalmente com o uso de painéis fotovoltaicos por parte destes consumidores. Além disto, com a redução dos custos dos medidores inteligentes de energia elétrica, estes consumidores também podem monitorar seu consumo em tempo real, promovendo ações de aumento de eficiência energética para reduzir custos. Mais recentemente, foram criadas as bandeiras tarifárias, que propõem identificar as condições sistêmicas por cores verde, amarela e vermelha. As cores amarela e vermelha sinalizam aumentos de custos na produção de energia elétrica e, consequentemente, são repassados para o consumidor na forma de aumento de tarifa, promovendo resposta da demanda. Assim, há uma razão adicional para os consumidores monitorarem seu consumo. Não obstante, em 2018 foi adotada uma nova modalidade tarifária voltada para esta classe de consumidor chamada tarifa branca. Nesta modalidade, o consumidor possui diferentes valores de tarifas para diferentes períodos do dia. Assim, o consumidor que optar por esta modalidade pode reduzir o custo da sua fatura deslocando o consumo de horários de maior valor de tarifa para horários de menor valor de tarifa. Esta dissertação busca analisar em detalhes a viabilidade de um consumidor residencial migrar seu contrato para a chamada tarifa branca. Para isto, é proposto um modelo de otimização linear inteiro misto que busca desagregar o consumo de energia elétrica, medido de forma não invasiva, do consumidor para os diferentes eletrodomésticos da casa. Logo, o consumidor poderá decidir pela mudança contratual avaliando a perda de conforto que terá em mudar seus hábitos de consumo. A aplicação do modelo proposto é interessante não só por apresentar um diagnóstico mais detalhado do consumo de energia elétrica, mas também por identificar o funcionamento de eletrodomésticos como geladeira, ar condicionado e frigobar, que possuem diferentes estados de operação que dificilmente seriam capturados por uma simples inspeção destes eletrodomésticos. Para ilustrar o modelo proposto, nesta dissertação, dados de um consumidor real foram utilizados e a acurácia do modelo pôde ser comprovada com medições diretas de alguns eletrodomésticos. Desta forma, o consumidor tem a sua disposição uma ferramenta de apoio à decisão importante para monitorar o funcionamento dos eletrodomésticos e definir se deve migrar para a nova modalidade tarifária. / [en] In the last years, energy consumption has increased significantly for consumers in Brazil. Large consumers, such as industrial and commercial customers, are currently subject to cost-mitigation alternatives such as demand contract optimization, power factor reduction, self-generation, renewable or non-renewable generation, and the possibility of migrating to the free market of electric energy, with various modes of purchase, prices and deadlines. The consumer, in which the means of the upper costs with the fat means of the data of the meters, is in function of minor engines to reduce their costs. However, on a constant basis, with the use of photovoltaic panels, by these consumers. In addition, with the help of the costs of smart electric power meters, these profits are potentially higher, in real time, the ability to generate weaker sound profits for the cost image. More recently, they were created as tariff plates, which identify the systemic conditions by the green, yellow and red nuclei. The yellow and red samples are generated from the temperature of electric energy production and, consequently, are passed on to the consumer in the form of temperature increase. Thus, there is a large difference in consumption levels of your consumption. Nevertheless, in 2015 a new tariff modality was implemented for this class of energy consumption called the white tariff. In this mode, the buyer has different rate values for different periods of the day. Thus, consumers who have this option can reduce the cost of their invoice in relation to the consumption of schedules of higher tariff value for the hours of lower tariff value. This dissertation looks at the analysis on a feasibility of a residential ad migrating its contract to a so-called white tariff. To this end, it is necessary a linear model that makes the difference in consumption of electric energy, measured non-invasively, from consumer to the different units of household appliances of the house. Therefore, the consumer is also evaluated by contracting a service that improves their consumption capacity. The application of the model is more interesting, but no longer presents the power of electric power, but also has the same standard of electricity as the refrigerator, air conditioning and minibar, which have different states of operation that are hardly captured by a simple inspection of each appliance. To illustrate the proposed model, this dissertation, data from a real consumer were used and an accuracy of the model can be proven with the direct measurements of some home appliances. The way in which the consumer has a migration support tool for the operation of the equipment and defines whether to migrate to a new tariff modality.

Dynamic Voltage Scaling (DVS) and Adaptive body bias (ABB) techniques respectively try to reduce the dynamic and static power components of an integrated circuit. Ideally, the two techniques can be combined to find the optimal operating voltages (VDD and VBB) to minimize power consumption. A combination of the DVS and ABB may warrant the circuit to operate at voltages (supply and body bias) different from the values specified by the two methods working independently. Also, this VDD and VBB values for minimal power consumption varies with the workload of the circuit. The workload can be used as an index to select the optimal VDD/VBB values to minimize the total power consumption. This paper examines the optimal voltages for minimal power operation for typical data path circuits like adders and multiply-accumulate (MAC) units across various process, voltage, and temperature conditions and under different workloads. In addition, a workload based look up table to minimize the power consumption is also proposed. Simulation results for an adder and a multiply-accumulate circuit block indicate a power saving of 12-30% over standard DVS scheme.

This thesis focuses on the design and implementation of a software tool able to achieve electricity grid optimal management in a dynamic pricing environment, at the residential level, and from a customer's perspective. The main drivers encouraging a development of energy management at the home level are analyzed, and a system architecture modeling power, thermodynamic and economic subsystems is proposed. The user behavior is also considered. A mathematical formulation of the related energy management optimization problem is proposed based on the linear programming theory. Several cases involving controllable and non-controllable domestic loads as well as renewable energy sources are presented and simulation scenarios illustrate the proposed optimization strategy in each case. The performance of the controller and the changes in energy use are analyzed, and ideas for possible future work are discussed.

High-income households are important for advancing energy efficiency in South Africa and yet little is known about how to encourage lower energy use behaviour in this group. This paper sets out the case for wide-scale research into how to encourage high-income earners to be more energy efficient behaviour in the home and presents the results of a prototype study. Behaviour change research offers no one framework for investigating behaviour in this group. However, the Theory of Planned Behaviour is a model, which has been successfully employed to understand and formulate behaviour interventions across a wide range of subject, including household energy use. In order to understand the potential of this model as a way of investigating how to encourage energy efficient household behaviour of high-income earners, a study investigating the model's practical and theoretical issues and benefits was undertaken . Component A sets the case for the important role high-income earners can play in achieving energy efficiency targets, summarises the history of relevant psychological research and establishes a methodology for the study. Component B summarises the case for the study and presents the research results and lessons learned in the style of a journal paper. The results suggest that the model has promise. Attitudes, Subjective Norms and Perceived Behavioural Controls accounted for 63.7% of the variance in intention of the sample to be energy efficient in the home. However, the study indicates that the model, although useful, is not sufficient for understanding actual behaviour and informing appropriate practica l interventions. Consequently a number of suggestions are made as to how to design a future research approach. / Thesis (M.Env.Dev.)-University of KwaZulu-Natal, Pietermaritzburg, 2005.

Dissertação apresentada a Coordenação dos Programas de Pós-Graduação de Engenharia da Universidade Federal do Rio de Janeiro para a obtenção do grau de Mestre em Ciências em Planejamento Energético. / Dissertação (mestrado) - Universidade Federal do Rio de Janeiro, Coordenação dos Programas de Pós-Graduação de Engenharia, Rio de Janeiro, 2006. / Bibliografia: p.124-141 / Esta dissertação tem como principal objetivo analisar a viabilidade econômica da substituição dos sistemas convencionais de aquecimento de água por sistemas solares no setor hoteleiro, especificamente, em resorts, que possuem características de operação e consumo peculiares devido à grande diversidade de serviços oferecidos. Como a eficiência dos sistemas solares depende, dentre outros fatores, do grau de insolação e radiação solar da região em que vão ser implantados, escolheu-se a região Nordeste do Brasil devido às suas condições climáticas favoráveis. Os resorts têm grande preocupação com questões ambientais e um forte apelo ecológico, o que facilitaria a penetração de uma fonte de energia renovável, como a solar. / This dissertation aims at evaluating the economical viability of conventional water heating systems’ substitution by solar systems in hotels, particularly in resorts, whose operational and energetic characteristics show huge diversity of services. As the solar systems’ efficiency depends, among other factors, on the insolation degree and solar radiation at the installation local, the Brazilian Northeast region was chosen due to its favorable climate conditions. Resorts have a great concern about environment issues that would facilitate the penetration of a renewable energy source, such as the solar energy. It was researched the technology’s state of art and its installed capacity worldwide; a description of Brazilian hotels was made and it was evaluated the impact of the substitution of conventional water heating systems by the solar one in the sector energy consumption, through the savings perceived by the hotel during 20 years. An analysis to verify the results’ sensibility to some variables was also made and the final results confirm the project viability.

Este trabalho estuda o impacto energético da otimização do tamanho do payload de códigos convolucionais em um enlace sem fio ponto-a-ponto dentro de uma rede de sensores. Consideram-se dois modelos de canal, AWGN e Rayleigh, visando representar cenários com diferentes características quanto à severidade de enlace. Nesse contexto, faz-se o estudo da otimização da relação sinal-ruído, da taxa de código empregada, bem como a otimização do tamanho do payload para diferentes condições de transmissão. Os dados numéricos obtidos através de simulação demonstram que existe um ponto ótimo para o tamanho do payload, que varia com a distância de transmissão e que proporciona ganhos em termos de eficiência energética, especialmente em enlaces de curta distância. / This paper studies the impact of the payload size in the energy efficiency in a point-to-point link in a wireless sensor network using convolutional codes. Two channel models are considered, AWGN and Rayleigh, representing distinct conditions with respect to the severity of the link. In this context, signal-to-noise ratio, code rate and payload size are optimized. The numeric results obtained through simulations show that there is an optimal point for the payload size, which depends on the transmission distance, and which provides gains in the overall energy efficiency, especially in short range links.

CAPES / Um dos aspectos fundamentais da inteligência natural é sua aptidão no processamento de informações temporais. O grande desafio proposto é o de desenvolver sistemas inteligentes que mapeiem essa aptidão do comportamento humano. Neste contexto, aportam as Máquinas de Estado Líquido (LSMs), uma arquitetura neural pulsada (meio líquido) que projeta os dados de entrada em um espaço dinâmico de alta dimensão e, por conseguinte, realiza a análise do conjunto de dados de entrada através de uma rede neural clássica (unidade de leitura). Desta maneira, esta tese apresenta uma solução inovadora para a previsão de séries temporais contínuas através das LSMs com mecanismo de reinicialização e entradas analógicas, contemplando a área da demanda de energia elétrica. A metodologia desenvolvida foi aplicada no horizonte de previsão a curto prazo e a longo prazo. Os resultados obtidos são promissores, considerando o alto erro estabelecido para parada do treinamento da unidade de leitura, o baixo número de iterações do treinamento da unidade de leitura e que nenhuma estratégia de ajustamento sazonal, ou pré-processamento, sob os dados de entrada foi realizado. Até o momento, percebe-se que as LSMs têm despontado como uma nova e promissora abordagem dentro do paradigma conexionista, emergente da ciência cognitiva. / Among of several aspects of the natural intelligence is its ability to process temporal information. One of major challenges to be addresses is how to efficiently develop intelligent systems that integrate the complexities of human behavior. In this context, appear the Liquid State Machines (LSMs), a pulsed neural architecture (liquid) that projects the input data in a high-dimensional dynamical space and therefore makes the analysis of input data all through a classical neural network (readout). Thus, this thesis presents an innovative solution for forecasting continuous time series through LSMs with reset mechanism and analog inputs, applied to the electric energy demand. The methodology was applied in the short-term and long-term forecasting of electrical energy demand. Results are promising, considering the high error to stop training the readout, the low number of iterations of training of the readout, and that no strategy of seasonal adjustment or preprocessing of input data was achieved. So far, it can be notice that the LSMs have been studied as a new and promising approach in the Artificial Neural Networks paradigm, emergent from cognitive science.

Neste trabalho é apresentada uma abordagem de controle de demanda para consumidores residenciais de baixa tensão, visando melhoria da eficiência energética em Smart Grids. Inicialmente, um emulador de cargas elétricas residenciais é modelado com base na literatura. O emulador é composto pelo modelo de um reservatório de aquecimento de água (boiler), o modelo de um aparelho de ar condicionado e também modelos de consumo de iluminação, televisores e uma geladeira. Utilizando o software Matlab foi realizada a implementação e simulação do emulador. Os principais algoritmos de controle de demanda são investigados, a fim de verificar o seu desempenho quando aplicados ao conjunto de cargas residenciais. Esses algoritmos normalmente realizam o controle de demanda a partir de um sistema de prioridades. Ainda, a partir dessa analise demostra-se que estes algoritmos consideram níveis de conforto do usuário, porém não permitem o acionamento de duas ou mais cargas em um mesmo período caso a demanda da residência ultrapasse um limite predeterminado. Portanto, propõem-se um algoritmo de controle de demanda adaptativo que utiliza o método de busca Rosenbrock, com o objetivo de sobrepujar tais limitações. O procedimento proposto realiza a operação das cargas residenciais de forma gradual considerando níveis de prioridade e parâmetros de conforto dos usuários. Demonstra-se através de simulações e experimentos que através do método proposto é possível realizar a ativação de diversas cargas concorrentemente, desde que respeitados os níveis de conforto e de demanda. Para obtenção dos resultados experimentais o controlador de demanda foi implementado em um sistema embarcado e testado com o emulador de cargas elétricas residenciais implementado em uma arquitetura HIL (Hardware-in-the-loop). Analisando os resultados, observou-se que o consumo de energia foi o mesmo para todos os cenários simulados sendo que a demanda se manteve abaixo dos limites parametrizados. Porém com o limitador de demanda ativo, se obteve uma redução de até 52% no tempo de aquecimento da água utilizando o controlador de demanda adaptativo, dessa forma o desconforto dos usuários pode ser minimizado. / This work presents an approach to control demand for residential low voltage consumers, aiming to improve energy efficiency in Smart Grids. Initially, an emulator of residential electric loads is modeled based on the literature. The emulator consists of a reservoir for water heating model, the model of an air conditioner and also models of consumption for lighting, televisions and a refrigerator. The implementation and simulation were performed using software Matlab. The demand control algorithms are investigated in order to verify its performance when applied to the set of residential loads. These algorithms typically perform control demand from a system of priorities. Still, from this analysis it demonstrates that these algorithms consider levels of user comfort, but do not allow the drive of two or more loads in the same period of residence if the demand exceeds the limit. Therefore, we propose a control algorithm that uses Rosenbrock search of demand adaptive method, aiming to overcome these limitations. The proposed procedure performs the operation of residential loads gradually considering priority levels and parameters of comfort of users. It is shown through simulations and experiments using the proposed method can perform the activation of several concurrently loads, provided they comply with the limits of comfort and demand. To obtain the experimental results demand the controller was implemented in an embedded system and tested with the emulator residential electrical loads implemented in a HIL (Hardware-in-theloop) architecture. Analyzing the results, it was observed that the power consumption is the same for all scenarios simulated and demand remained below parametric limits. But with the demand limiter active, we obtained a reduction of up to 52% in heat water using the demand controller adaptive, so the discomfort of the users can be minimized.