Applying fuzzy logic and neural networks to forecasting in efficiency programs

This paper addresses the design and implementation of Fuzzy Inference System (FIS) and Artificial Neural Network (ANN) to determine daily demand curves of residential Electric Showers (ESs). To determine the daily curves were used two inputs: shower duration and number of showers. In Brazil the residential electricity corresponds to 25% of all demand. The use of ESs is widespread, it represents about 22% of the total residential consumption. This work evaluates the impacts of Energy Efficiency Programs (EEPs) in low-income communities located in the state of Rio de Janeiro in Brazil. Additionally, two different ESs devices are compared: the ES Temperature Control (ESTC) and the ES Heat Recovery (ESHR). This study was based on measurements made in 60 households in different low-income neighbourhoods. The results showed that ANN makes better predictions, however both FIS and ANN have the capacity to determine rapid changes in peak demand. These tools can be used in small and mediumsized areas with similar socio-economic features which allow determining the impact of EEPs in the communities in advance. Furthermore, the application of these techniques can be of help in the actions of Demand Side Management (DSM) mainly during the maximum demand period

Fuzzy logic and applied neural networks for prediction of charge curves of electric showers in energy efficiency programs in Brazil

Orientadores: Gilberto de Martino Jannuzzi, Conrado Augustus de MeloTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia MecânicaResumo: O consumo de energia elétrica do setor residencial corresponde à, aproximadamente, 25% da demanda energética total do Brasil. Um dos principais usos da energia nesse setor é o aquecimento de água para banho, sendo os chuveiros elétricos (CEs) os principais aparelhos usados para essa finalidade. De fato, o consumo dos CEs representa em média 21% do consumo do setor residencial. Nas regiões mais frias do país, Sul e Sudeste, com aproximadamente 110 milhões de habitantes, o consumo energético devido ao uso dos CEs é maior, atingindo 25 e 26%, respectivamente. Os CEs são principalmente usados das 18h às 21h e, por serem aparelhos que consomem bastante energia elétrica, com potência entre 4 e 8 kW, causam o maior incremento na demanda máxima no sistema elétrico brasileiro. Nas comunidades de baixa renda, o consumo de eletricidade devido aos CEs é crítico, já que a mesma pode representar em torno de 23% da conta de energia elétrica. Neste trabalho, foi feita a concepção e a implementação de um Sistema de Inferência Fuzzy (SIF) e de uma Rede Neural Artificial (RNA) para determinar a demanda de curva diária dos CEs. Foi avaliado o impacto dos Programas de Eficiência Energética (PEEs) da Agência Nacional de Energia Elétrica (ANEEL) em comunidades de baixa renda, localizadas no Estado do Rio de Janeiro, nas cidades de Rio de Janeiro e Volta Redonda. O estudo foi desenvolvido a partir de medições em 75 residências. Adicionalmente, dois tipos de CEs foram comparados o controlador eletrônico de temperatura (CECET) e o recuperador de calor (CERC). Os resultados obtidos pelos métodos SIF e RNA foram validados usando o erro absoluto médio (EAM) e o erro porcentual absoluto médio (EMPA). Essa validação mostrou que, na maioria dos casos, os resultados das simulações reproduziram adequadamente a demanda de curva diária dos CEs. O uso de técnicas não tradicionais, tais como Fuzzy e RNAs, pode contribuir significativamente à pesquisa e a avaliação dos PEEs da ANEEL, no Brasil. Esse tipo de ferramenta pode ser usado em comunidades de tamanho pequeno, com condições socioeconômicas similares, permitindo prever o impacto dos PEEs na economia de energia e na redução da potência máximaAbstract: The electricity consumption in the residential sector in Brazil, corresponds to approximately 25% of the total electricity demand. The electric showers (ES) represent, on average, about 21% of the total residential electricity consumption. In colder regions, such as the Southeast and South, the electricity demand of ESs is even higher, about 26% and 25%, respectively. About 110 million people live in these regions, where the use of ES is widespread. The power consumption of ESs varies between 4-8 kW. In the residential sector, the ESs are used mainly from 18:00 to 21:00 hours, which is accountable for the maximum peak demand in the Brazilian electrical system. The use of ESs in terms of electricity costs is very significant, mainly in low-income households, where the electricity consumption of ESs represents about 23% of the monthly electricity bill. This thesis addresses the design and implementation of a Fuzzy Inference System (FIS) and an Artificial Neural Network (ANN) to determine daily demand curves of residential ESs. The present study evaluated the impacts of Energy Efficiency Programs (EEPs) in low-income communities located in two cities in the state of Rio de Janeiro ¿ Rio de Janeiro and Volta Redonda. Additionally, two different ES devices are compared: the electric shower with temperature control (ESTC) and the electric shower with heat recovery (ESHR). The study was based on measurements made in 75 households in different low-income neighborhoods. Forecasting results were validated using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). This validation showed that, in most cases, the results were accurate. The use of non-traditional techniques, such as FIS and ANN, can contribute significantly to the investigation and assessment of EEPs in developing countries. These tools can be used in small and medium-sized communities with similar socio-economic conditions, which allow for the determination, in advance, of the impact of EEPs in energy saving and reduction of peak demand in the communities. We conclude that accurate models to forecast electricity consumption are indispensable for electric utilities in the process of design, planning and operating power systems. Furthermore, these forecasting approaches allow decision-makers to implement EEP-related targetsDoutoradoPlanejamento de Sistemas EnergeticosDoutor em Planejamento de Sistemas Energético