Electrical energy consumption has been an ongoing research area since the coming of smart homes and Internet of Things devices. Consumption characteristics and usages profiles are directly influenced by building occupants and their interaction with electrical appliances. Extracted information from these data can be used to conserve energy and increase user comfort levels. Data analysis together with machine learning models can be utilized to extract valuable information for the benefit of occupants themselves, power plants, and grid operators. Public energy datasets provide a scientific foundation to develop and benchmark these algorithms and techniques. With datasets exceeding tens of terabytes, we present a novel study of five whole-building energy datasets with high sampling rates, their signal entropy, and how a well-calibrated measurement can have a significant effect on the overall storage requirements. We show that some datasets do not fully utilize the available measurement precision, therefore leaving potential accuracy and space savings untapped. We benchmark a comprehensive list of 365 file formats, transparent data transformations, and lossless compression algorithms. The primary goal is to reduce the overall dataset size while maintaining an easy-to-use file format and access API. We show that with careful selection of file format and encoding scheme, we can reduce the size of some datasets by up to 73%.