Energy conservation and preventive maintenance of equipment require the ability to
accurately predict future trends in shop floor power consumption to keep track of equipment …

Non-intrusive load monitoring (NILM) provides insights into how much energy consumers
are consuming, encouraging them to make energy-saving changes. Load forecasting, on the …

Based on multi-period-sequential-index combination (MPSIC) technology, three forecasting
methods (auto-MPSIC, IV-MPSIC, MSEI-MPSIC) were proposed for short-term prediction of …

The Convolutional Neural Network (CNN) model is one of the most effective models for load
forecasting with hyperparameters which can be used not only to determine the CNN …

This study investigates data standardization methods based on the grid search (GS)
algorithm for energy load forecasting, including zero-mean, min-max, max, decimal, sigmoid …

In this paper, the Multi-view ConvLSTM Neural Network based on multi view convolution is
proposed to solve the problems of low accuracy and poor stability of power load forecasting …

Building energy consumption can be considered as time series, which are predicted using
time series analysis methods. There are lots of traditional time series prediction algorithms …