A comparison of univariate methods for forecasting electricity demand up to a day ahead

This empirical paper compares the accuracy of six univariate methods for short-term electricity demand forecasting for lead times up to a day ahead. The very short lead times are of particular interest as univariate methods are often replaced by multivariate methods for prediction beyond about six hours ahead. The methods considered include the recently proposed exponential smoothing method for double seasonality and a new method based on principal component analysis (PCA). The methods are compared using a time series of hourly demand for Rio de Janeiro and a series of half-hourly demand for England and Wales. The PCA method performed well, but, overall, the best results were achieved with the exponential smoothing method, leading us to conclude that simpler and more robust methods, which require little domain knowledge, can outperform more complex alternatives

Activation of BMP-Smad1/5/8 Signaling Promotes Survival of Retinal Ganglion Cells after Damage In Vivo

While the essential role of bone morphogenetic protein (BMP) signaling in nervous system development is well established, its function in the adult CNS is poorly understood. We investigated the role of BMP signaling in the adult mouse retina following damage in vivo. Intravitreal injection of N-Methyl-D-aspartic acid (NMDA) induced extensive retinal ganglion cell death by 2 days. During this period, BMP2, -4 and -7 were upregulated, leading to phosphorylation of the downstream effector, Smad1/5/8 in the inner retina, including in retinal ganglion cells. Expression of Inhibitor of differentiation 1 (Id1; a known BMP-Smad1/5/8 target) was also upregulated in the retina. This activation of BMP-Smad1/5/8 signaling was also observed following light damage, suggesting that it is a general response to retinal injuries. Co-injection of BMP inhibitors with NMDA effectively blocked the damage-induced BMP-Smad1/5/8 activation and led to further cell death of retinal ganglion cells, when compared with NMDA injection alone. Moreover, treatment of the retina with exogenous BMP4 along with NMDA damage led to a significant rescue of retinal ganglion cells. These data demonstrate that BMP-Smad1/5/8 signaling is neuroprotective for retinal ganglion cells after damage, and suggest that stimulation of this pathway can serve as a potential target for neuroprotective therapies in retinal ganglion cell diseases, such as glaucoma

Prediction models for short-term load and production forecasting in smart electrical grids

The scheduling of household smart load devices play a key role in microgrid ecosystems, and particularly in underpowered grids. The management and sustainability of these microgrids could bene t from the application of short-term prediction for the energy production and demand, which have been successfully applied and matured in larger scale systems, namely national power grids. However, the dynamic change of energy demand, due to the necessary adjustments aiming to render the microgrid self-sustainability, makes the forecasting process harder. This paper analyses some prediction techniques to be embedded in intelligent and distributed agents responsible to manage electrical microgrids, and especially increase their self-sustainability. These prediction techniques are implemented in R language and compared according to di erent prediction and historical data horizons. The experimental results shows that none is the optimal solution for all criteria, but allow to identify the best prediction techniques for each scenario and time scope.info:eu-repo/semantics/publishedVersio

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