Short-term electricity demand forecasting based on multiple LSTMs

In recent years, the problem of unbalanced demand and supply in electricity power industry has seriously affected the development of smart grid, especially in the capacity planning, power dispatching and electric power system control. Electricity demand forecasting, as a key solution to the problem, has been widely studied. However, electricity demand is influenced by many factors and nonlinear dependencies, which makes it difficult to forecast accurately. On the other hand, deep neural network technologies are developing rapidly and have been tried in time series forecasting problems. Hence, this paper proposes a novel deep learning model, which is based on the multiple Long Short-Term Memory (LSTM) neural networks to solve the problem of short-term electricity demand forecasting. Compared with autoregressive integrated moving average model (ARIMA) and back propagation neural network (BPNN), our model demonstrates competitive forecast accuracy, which proves that our model is promising for electricity demand forecasting

Diversification of self-replicating molecules

How new species emerge in nature is still incompletely understood and difficult to study directly. Self-replicating molecules provide a simple model that allows us to capture the fundamental processes that occur in species formation. We have been able to monitor in real time and at a molecular level the diversification of self-replicating molecules into two distinct sets that compete for two different building blocks ('food') and so capture an important aspect of the process by which species may arise. The results show that the second replicator set is a descendant of the first and that both sets are kinetic products that oppose the thermodynamic preference of the system. The sets occupy related but complementary food niches. As diversification into sets takes place on the timescale of weeks and can be investigated at the molecular level, this work opens up new opportunities for experimentally investigating the process through which species arise both in real time and with enhanced detail