Energy management systems allow the Smart Grids industry to track, improve, and regulate energy use. Particularly, demand-side management is regarded as a crucial component of the entire Smart Grids system. Therefore, by aligning utility offers with customer demand, anticipating future energy demands is essential for regulating consumption. An updated examination of several forecasting techniques for projecting energy short-term load forecasts is provided in this article. Each class of algorithms, including statistical techniques, Machine Learning, Deep Learning, and hybrid combinations, are comparatively evaluated and critically analyzed, based on three real consumption datasets from Spain, Germany, and the United States of America. To increase the size of tiny training datasets, this paper also proposes a data augmentation technique based on Generative Adversarial Networks. The results show that the Deep Learning-hybrid model is more accurate than traditional statistical methods and basic Machine Learning procedures. In the same direction, it is demonstrated that more comprehensive datasets assisted by complementary data, such as energy generation and weather, may significantly boost the accuracy of the models. Additionally, it is also demonstrated that Generative Adversarial Networks-based data augmentation may greatly improve algorithm accuracy.

Cognitive Radio networks are emerging as a new paradigm of communication and channels sharing in multimedia and wireless networks. In this paper, we consider multimedia traffic transmission over shared Cognitive Radio (CR) networks using fountain codes under different subchannel selection policies in a fading environment. We assume that the primary traffic arrival follows a Poisson process. To share this CR infrastructure, we propose to make use of the Time Division Multiple Access (TDMA) method, each secondary user (SU) is assigned one time slot where he transmits with a certain probability, the proposed model permits to this SU to transmits also in the remaining slots if he has data to transmit and has missed his own slot. Therefore, packets are not only lost by reason of Primary traffic interruptions, but also we consider losses caused by collisions between several SUs due to our specific time slot allocation. We evaluate the influence of the proposed model parameter settings on the secondary traffic transmission performance in view of the Spectral Efficiency. Numerical simulations show that the system still exhibit good secondary traffic efficiency despite of the presence of primary interferences and secondary collisions.

The evolution toward the so-called smart grid (SG) needs to be supported by robust and cost-efficient advanced metering infrastructure. This article focuses on technology selection and gateway placement in hybrid SGs, wherein each smart meter (SM) is equipped with either a power line communication or a long-range wide area network protocol node to connect to its serving gateway. The latter is endowed with both technologies. In particular, we introduce an analytical framework to characterize the performance–cost tradeoffs using a multicriteria optimization approach. For each technology, a set of performance objective functions (i.e., sum-rate and max–min) has been derived based on the ergodic capacity, whereas the cost is represented by SG hardware expenses. Then, the nondominated sorting genetic algorithm-II has been exploited using an appropriate chromosome structure to provide the suitable access technology for each SM along with the optimal number and the placement of the serving GWs. Extensive numerical simulations have shown that hybrid solutions provide a good interplay between performance and investment costs, spurring the need to redefine the conventional SG designs traditionally operating in single communication technologies.

In this paper, we treat the problem of delay constrained multimedia applications over Cognitive Radio (CR) networks under Binomial primary traffic arrival. Secondary Users (SUs) share the spectrum holes using the TDMA method based on the Opportunistic Spectrum Access. Primary traffic interruptions and the concurrent secondary access to spectral resources are major reasons for packet losses. Thereby, we use a specific packetization framework for Multiple Description Coding to cope with both primary interruptions and secondary collisions. Numerical simulations, in view of the Spectral Efficiency, show the effectiveness of the proposed solution in dealing with dynamics between primary and secondary users.