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. ud
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机译：介绍了英国家用电器负荷曲线的工程和统计人工神经网络组合模型的开发。该模型使用日记式设备使用数据和2010年夏季分别从51个郊区家庭和46个农村家庭收集的调查问卷。它还结合了测得的能量数据，并且对社会经济，物理住所和温度变量敏感。使用带有反向传播训练的两层前馈网络在MATLAB中构建原型模型，该网络具有12:10:24架构。模型输出包括可应用于能源计划（微型可再生能源和智能电网），建筑模拟工具和能源政策等领域的设备负载曲线。 ud
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