A combined engineering and statistical model of UK domestic appliance electrical load profiles

The development of a combined engineering and statistical Artificial Neural Network model of UK domestic appliance load profiles is presented. The model uses diary-style appliance use data and a survey questionnaire collected from 51 suburban households and 46 rural households during the summer of 2010 and2011 respectively. It also incorporates measured energy data and is sensitive to socioeconomic, physical dwelling and temperature variables. A prototype model is constructed in MATLAB using a two layer feed forward network with back propagation training which has a 12:10:24 architecture. Model outputs include appliance load profiles which can be applied to the fields of energy planning (microrenewables and smart grids), building simulation tools and energy policy.

A novel neural model to electrical load forecasting in transformers

The paper describes a novel neural model to electrical load forecasting in transformers. The network acts as identifier of structural features to forecast process. So that output parameters can be estimated and generalized from an input parameter set. The model was trained and assessed through load data extracted from a Brazilian Electric Utility taking into account time, current, tension, active power in the three phases of the system. The results obtained in the simulations show that the developed technique can be used as an alternative tool to become more appropriate for planning of electric power systems.

Electrical load forecasting formulation by a fast neural network

The objective of this work is to develop a methodology for electric load forecasting based on a neural network. Here, backpropagation algorithm is used with an adaptive process that based on fuzzy logic and using a decaying exponential function to avoid instability in the convergence process. This methodology results in fast training, when compared to the conventional formulation of backpropagation algorithm. The results are presented using data from a Brazilian Electric Company, and shows a very good performance for the proposal objective.

A Cellular Automaton Approach to Spatial Electric Load Forecasting

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A fast electric load forecasting using adaptive neural networks

This work presents a procedure for electric load forecasting based on adaptive multilayer feedforward neural networks trained by the Backpropagation algorithm. The neural network architecture is formulated by two parameters, the scaling and translation of the postsynaptic functions at each node, and the use of the gradient-descendent method for the adjustment in an iterative way. Besides, the neural network also uses an adaptive process based on fuzzy logic to adjust the network training rate. This methodology provides an efficient modification of the neural network that results in faster convergence and more precise results, in comparison to the conventional formulation Backpropagation algorithm. The adapting of the training rate is effectuated using the information of the global error and global error variation. After finishing the training, the neural network is capable to forecast the electric load of 24 hours ahead. To illustrate the proposed methodology it is used data from a Brazilian Electric Company. © 2003 IEEE.

Spatial electric load forecasting using a local movement approach

An agent based model for spatial electric load forecasting using a local movement approach for the spatiotemporal allocation of the new loads in the service zone is presented. The density of electrical load for each of the major consumer classes in each sub-zone is used as the current state of the agents. The spatial growth is simulated with a walking agent who starts his path in one of the activity centers of the city and goes to the limits of the city following a radial path depending on the different load levels. A series of update rules are established to simulate the S growth behavior and the complementarity between classes. The results are presented in future load density maps. The tests in a real system from a mid-size city show a high rate of success when compared with other techniques. The most important features of this methodology are the need for few data and the simplicity of the algorithm, allowing for future scalability. © 2009 IEEE.

Is the electric vehicle a solution for the wind power integration in the portuguese power system?

The integration of Plug-in electric vehicles in the transportation sector has a great potential to reduce oil dependency, the GHG emissions and to contribute for the integration of renewable sources into the electricity generation mix. Portugal has a high share of wind energy, and curtailment may occur, especially during the off-peak hours with high levels of hydro generation. In this context, the electric vehicles, seen as a distributed storage system, can help to reduce the potential wind curtailments and, therefore, increase the integration of wind power into the power system. In order to assess the energy and environmental benefits of this integration, a methodology based on a unit commitment and economic dispatch is adapted and implemented. From this methodology, the thermal generation costs, the CO2 emissions and the potential wind generation curtailment are computed. Simulation results show that a 10% penetration of electric vehicles in the Portuguese fleet would increase electrical load by 3% and reduce wind curtailment by only 26%. This results from the fact that the additional generation required to supply the electric vehicles is mostly thermal. The computed CO2 emissions of the EV are 92 g CO2/kWh which become closer to those of some new ICE engines.

Previsão do Diagrama de Carga de Subestações da REN utilizando Redes Neuronais

A presente dissertação apresenta o estudo de previsão do diagrama de carga de subestações da Rede Elétrica Nacional (REN) utilizando redes neuronais, com o intuito de verificar a viabilidade do método utilizado, em estudos futuros. Atualmente, a energia elétrica é um bem essencial e desempenha um papel fundamental, tanto a nível económico do país, como a nível de conforto e satisfação individual. Com o desenvolvimento do setor elétrico e o aumento dos produtores torna-se importante a realização da previsão de diagramas de carga, contribuindo para a eficiência das empresas. Esta dissertação tem como objetivo a utilização do modelo das redes neuronais artificiais (RNA) para criar uma rede capaz de realizar a previsão de diagramas de carga, com a finalidade de oferecer a possibilidade de redução de custos e gastos, e a melhoria de qualidade e fiabilidade. Ao longo do trabalho são utilizados dados da carga (em MW), obtidos da REN, da subestação da Prelada e dados como a temperatura, humidade, vento e luminosidade, entre outros. Os dados foram devidamente tratados com a ajuda do software Excel. Com o software MATLAB são realizados treinos com redes neuronais, através da ferramenta Neural Network Fitting Tool, com o objetivo de obter uma rede que forneça os melhores resultados e posteriormente utiliza-la na previsão de novos dados. No processo de previsão, utilizando dados reais das subestações da Prelada e Ermesinde referentes a Março de 2015, comprova-se que com a utilização de RNA é possível obter dados de previsão credíveis, apesar de não ser uma previsão exata. Deste modo, no que diz respeito à previsão de diagramas de carga, as RNA são um bom método a utilizar, uma vez que fornecem, à parte interessada, uma boa previsão do consumo e comportamento das cargas elétricas. Com a finalização deste estudo os resultados obtidos são no mínimo satisfatórios. Consegue-se alcançar através das RNA resultados próximos aos valores que eram esperados, embora não exatamente iguais devido à existência de uma margem de erro na aprendizagem da rede neuronal.

Pientalojen ilmalämpöpumput sähköverkossa

Työssä tutkitaan ilmalämpöpumppujen kokonaisvaltaista vaikutusta sähköverkkoon. Tarkastelu aloitetaan lämpöpumppujen toiminnasta ja rakenteesta, josta jatketaan laitteen käytettävyyteen ja muiden lämmitysmenetelmien vertailuun. Sähköisten ominaisuuksien tarkastelussa pohditaan ilmalämpöpumppujen vaikutusta suomalaiseen sähköverkkoon muun muassa yleissähkötekniikan, taloudellisuuden ja energiatehokkuuden sekä häiriöiden kannalta. Tämä tutkielma rajoittuu pientaloihin, ja niihin asennettuihin ilma-ilmalämpöpumppuihin. Työn loppupäätelmänä on, että ilmalämpöpumppujen käytöstä ei juuri aiheudu vaikutuksia suomalaiseen sähköverkkoon. Suurimmat ilmalämpöpumppujen käytöstä syntyvät seuraukset kohdistuvat sähköverkkoyhtiöihin, joihin ilmalämpöpumput aiheuttavat taloudellisia menetyksiä. Suuret ja tulevaisuudessa kasvavat ilmalämpöpumppumäärät aiheuttavat sähköntuotantoon lisätehontarvetta huippukuorman aikaan. Toisaalta välitehoalueella tehontarve sekä energiankulutus pienenevät. Sähköverkoissa ei ole toistaiseksi havaittu ilmalämpöpumpuista johtuvia häiriöitä.

Performance of small wind turbines: simulation of electricity supply to loads connected to the public or isolated grid

ABSTRACT The successful in the implementation of wind turbines depends on several factors, including: the wind resource at the installation site, the equipment used, project acquisition and operational costs. In this paper, the production of electricity from two small wind turbines was compared through simulation using the computer software HOMER - a national model of 6kW and an imported one of 5kW. The wind resources in three different cities were considered: Campinas (SP/BR), Cubatão (São Paulo/BR) and Roscoe (Texas/ USA). A wind power system connected to the grid and a wind isolated system - batteries were evaluated. The results showed that the energy cost ($/kWh) is strongly dependent on the windmill characteristics and local wind resource. Regarding the isolated wind system – batteries, the full supply guarantee to the simulated electrical load is only achieved with a battery bank with many units and high number of wind turbines, due to the intermittency of wind power.

Design and assessment of a large commercial Photovoltaic System in Barbados

One of the main aims of this thesis is to design an optimized commercial Photovoltaic (PV) system in Barbados from several variables such as racking type, module type and inverter type based on practicality, technical performance as well as financial returns to the client. Detailed simulations are done in PVSYST and financial models are used to compare different systems and their viability. Once the preeminent system is determined from a financial and performance perspective a detailed design is done using PVSYST and AutoCAD to design the most optimal PV system for the customer. In doing so, suitable engineering drawings are generated which are detailed enough for construction of the system. Detailed cost with quotes from relevant manufacturers, suppliers and estimators become instrumental in determining Balance of System Costs in addition to total project cost. The final simulated system is suggested with a PV capacity of 425kW and an inverter output of 300kW resulting in an array oversizing of 1.42. The PV system has a weighted Performance Ratio of 77 %, a specific yield of 1467 kWh/kWp and a projected annual production of 624 MWh/yr. This system is estimated to offset approximately 28 % of Carlton’s electrical load annually. Over the course of 20 years the PV system is projected to produce electricity at a cost of $0.201USD/kWh which is significantly lower than the $0.35 USD/kWh paid to the utility at the time of writing this thesis. Due to the high cost of electricity on the island, an attractive Feed-In-Tariff is not necessary to warrant the installation of a commercial System which over a lifetime which produces electricity at less than 60% of the cost to the user purchasing electricity from the utility. A simple payback period of 5.4 years, a return on investment of 17 % without incentives, in addition to an estimated diversion of 6840 barrels of oil or 2168 tonnes of CO2 further provides compelling justification for the installation of a commercial Photovoltaic System not only on Carlton A-1 Supermarket, but also island wide as well as regionally where most electricity supplies are from imported fossil fuels.

Desenvolvimento de um sistema dinâmico para predição de cargas elétricas por redes neurais através do paradigma de programação orientada a objeto sob a linguagem JAVA

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Previsão de cargas elétricas através de um modelo híbrido de regressão com redes neurais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Previsão de cargas elétricas através de uma Rede Neural Híbrida Back-ART Fuzzy

Pós-graduação em Engenharia Elétrica - FEIS

Previsão de carga multinodal usando a rede neural ART-ARTMAP fuzzy

Pós-graduação em Engenharia Elétrica - FEIS