Predicting the electricity consumption of proposed new supermarkets is helpful to design and plan future energy management. Instead of creating complex site-specific thermal engineering models, data-driven energy prediction models can be useful to energy managers. We have designed and implemented a data-driven method to predict the future ’electricity daily load profile’ (EDLP) of new supermarkets using historical EDLPs of existing supermarkets of the same type. The supermarket features used for the prediction are 10 types of floor areas divided by usage ($m^2$) and its location. Four data-driven regression models are used and compared to predict EDLPs: Artificial Neural Networks, Support Vector Machines, k-Nearest Neighbours and OLS. Prediction computational experiments were performed over 1-h electricity readings of 213 UK supermarkets gathered during six years. Prediction error mainly varies between 12 and 20% depending on method, year, supermarket type, and division of the data (season or temperature intervals). EDLPs computed over warm periods are better predicted than over cold periods and supermarkets only with electricity are better predicted than supermarkets with electricity and gas. The three features with more weight in the prediction are Food, Chilled produce and Cafeteria areas. The limitations of machine learning methods to solve this problem are discussed.

预测拟建新超市的用电量有助于设计和规划未来的能源管理。代替创建复杂的特定于现场的热力工程模型，数据驱动的能源预测模型对能源管理者可能有用。我们已经设计并实施了一种数据驱动的方法，以使用相同类型的现有超级市场的​​历史EDLP来预测新超级市场的​​未来“每日电负荷概况”（EDLP）。用于预测的超市功能是按用途划分的10种类型的建筑面积（${米}^2$）及其位置。使用了四个数据驱动的回归模型并将其进行比较以预测EDLP：人工神经网络，支持向量机，k最近邻和OLS。对六年来收集的213家英国超市的1小时电读数进行了预测计算实验。预测误差主要在12％到20％之间变化，具体取决于方法，年份，超级市场类型和数据划分（季节或温度间隔）。与在寒冷时期相比，在温暖时期计算的EDLP更好地预测，而仅用电的超级市场比用电和天然气的超级市场更好。预测中权重较高的三个特征是食品，冷藏农产品和自助餐厅区域。讨论了机器学习方法解决该问题的局限性。