Low dimension active power load data using autoencoder

Dimensionality Reduction (DR) is key machine learning technique used to convert data from higher dimensional space to lower dimensionality space in order to build a predictive machine learning models with less number of model parameters. Original active power load dataset is prepared by collecting the data from 33/11KV substation near Godishala village in Telangana state, India. It consists total 12 features like L(T-1), L(T-2), L(T-3), L(T-4), L(T-24), L(T-48), L(T-72), L(T-96), Temperature, Humidity, Season and Day. This 12 features data is reconstructed in to 10 features using autoencoder with a training loss of 0.0061 and validation loss of 0.0062.

降维（DR）是关键机器学习技术，用于将数据从高维空间转换为低维空间，以便构建具有较少模型参数的预测性机器学习模型。原始的活性功率负载数据集是通过收集印度特伦甘纳邦Godishala村庄附近的33/11KV变电站的数据准备的。该数据集包含12个特征，如L(T-1)、L(T-2)、L(T-3)、L(T-4)、L(T-24)、L(T-48)、L(T-72)、L(T-96)、温度、湿度、季节和日期。这12个特征数据通过自动编码器重构为10个特征，训练损失为0.0061，验证损失为0.0062。