Utilizing the Koopman operator, the time-series output produced by a building simulation can be decomposed into spatial modes which capture the thermal behavior of a building at different time-scales.
Inputs to the HSI model consist of substrate distributions from river surveys, and water level and velocity time series, outputs from a hydrodynamic model.
To address this computational problem, this paper proposes a general statistical model as a computationally fast approximation for computer models with time series output.
In addition to the indices associated with the principal components, generalised sensitivity indices are proposed to synthesize the influence of each parameter on the whole time series output.
EPIC was able (a) to provide about 30% of the input data required for running the Pat-GEM model; and (b) to provide time series output data (with a daily time step) to calibrate the output variables biomass production and nutrient uptake.
This paper optimizes the time series output of temperature controlling devices through temperature adjustment by combining the dynamic variation law of adjusted temperature and power consumption, thus indirectly adjusting power load.
Finally, the value of wind power output, PV power output and load in the planning year can be calculated by using these normalized time series output multiplied by the predicted wind installed capacity, the predicted PV installed capacity and the predicted maximum load.
After mixing, the lock-in applies a low-pass filter to reduce noise contributions from unwanted frequencies outside the measurement bandwidth, thus the time series output of a lock-in amplifier is the result of a convolution of the lock-in amplifier's filter response with the demodulated input signal, i.e. (6).
The latter are calculated by numeric differentiation of the respective time series (output) data.
The resulting data provided to the NetGenerator algorithm are stored in X = e and U = e, i.e. matrices for the time series (output) and stimuli (input) data, respectively, for all experiments e = 1,…, E. Therefore, the dimensions are X = e : T e × N and U = e : T e × M with T e being the number of experimental time points, N being the number of time series and M being the number of inputs.
