… In this paper, firstly solar power prediction for a small scale PV panel is considered at
different time horizons. In general applications, 10 min or 1 h time horizons are used for data …

… Power Tossim benefits from the high scalability of Tossim, hardware abstraction in Tossim
results in a lack of detail and accuracy in the energy consumption prediction of Power … of small …

… Value prediction has been proposed for breaking datadependencies and … amount of energy.
In this study, we use data-widths to partition the value prediction table into several smaller …

… This locality induces shifts in the error distribution toward the upper left quadrant of the plot
such that a larger percentage of predictions has smaller errors. Similarly, Figure 7R indicates …

… the role of branch predictor organization in power/energy/… power in the branch predictor
if this results in more accurate … predictor, we find that the the energy savings are quite small…

… power production of a wind farm with three wind turbines, using real load data and comparing
different time prediction … Thus, small error in wind speed forecast will actually generate a …

… power prediction framework based on extensive power dynamic profiling and deep learning
models. In particular, we first analyse different power … power series we collected has a small …

… The power output of these PV farms may fluctuate due to a … In this paper, we propose a solar
power prediction model based … larger amount of clouds (near 100) and a smaller amount of …

… Our ensemble using all features yields the best MSE in four cases, but only takes a small
amount of the time taken by standard SVR. If training time is considered more important than …

… The smaller this variance is, the more precise is the prediction. Consistent with the definition
of E MAE in Eq. (31), the daily error (E DAY ) variance can be estimated as:(40) E DAY = 1 N …