Identifying hybrid heating systems in the residential sector from smart meter data

In this paper, we identify hybrid heating systems on a single residential customer’s premises using smart meter data. A comprehensive methodology is developed at a generic level for residential sector buildings to identify the type of primary and support heating systems. The methodology includes the use of unsupervised and supervised learning algorithms both separately and combined. It is applied to two datasets that vary in size, quality of data, and availability and reliability of background information. The datasets contain hourly electricity consumption profiles of residential customers together with the outdoor temperature. The validation metrics for the developed algorithms are elaborated to provide a probabilistic evaluation of the model. The results show that it is possible to identify the types of both primary and support heating systems in the form of probability of having electric- or non-electric type of heating. The results obtained help estimate the flexibility domain of the residential building sector and thereby generate a high value for the energy system as a whole

Identifying hybrid heating systems in the residential sector from smart meter data

Abstract In this paper, we identify hybrid heating systems on a single residential customer’s premises using smart meter data. A comprehensive methodology is developed at a generic level for residential sector buildings to identify the type of primary and support heating systems. The methodology includes the use of unsupervised and supervised learning algorithms both separately and combined. It is applied to two datasets that vary in size, quality of data, and availability and reliability of background information. The datasets contain hourly electricity consumption profiles of residential customers together with the outdoor temperature. The validation metrics for the developed algorithms are elaborated to provide a probabilistic evaluation of the model. The results show that it is possible to identify the types of both primary and support heating systems in the form of probability of having electric- or non-electric type of heating. The results obtained help estimate the flexibility domain of the residential building sector and thereby generate a high value for the energy system as a whole

Avaliação dos custos operacionais eficientes das empresas de transmissão do setor elétrico Brasileiro: uma proposta de adaptação do modelo dea adotado pela ANEEL

No setor elétrico brasileiro, as companhias de transmissão são remuneradas pela disponibilidade da capacidade de seus ativos, independentemente da quantidade de energia elétrica transmitida. Para induzir a operação eficiente das transmissoras, a ANEEL deve revisar periodicamente as receitas permitidas das transmissoras, considerando custos operacionais eficientes. Recentemente, a ANEEL publicou uma resolução em que descreve a metodologia utilizada no cálculo dos custos operacionais eficientes das transmissoras, a qual inclui um modelo de análise envoltória de dados (DEA). Neste trabalho propomos uma adaptação deste modelo DEA e apresentamos uma análise de sensibilidade dos resultados obtidos pelos dois modelos