Demand-side management in the smart grid often consists of optimizing energy-related objective functions, with respect to variables, in the presence of constraints expressing electrical consumption habits. These functions are often related to the user’s electricity invoice (cost) or to the peak energy consumption (peak-to-average energy ratio), which can cause electrical network failure on a large scale. However, the growth in energy demand, especially in emerging countries, is causing a serious energy crisis. This is why several studies focus on these optimization approaches. To our knowledge, no article aims to collect and analyze the results of research on peak-to-average energy consumption ratio and cost optimization using a systematic reproducible method. Our goal is to fill this gap by presenting a systematic mapping study on the subject, spanning the last decade (2013–2022). The methodology used first consisted of searching digital libraries according to a specific search string (104 relevant studies out of 684). The next step relied on an analysis of the works (classified using 13 criteria) according to 5 research questions linked to algorithmic trends, energy source, building type, optimization objectives and pricing schemes. Some main results are the predominance of the genetic algorithms heuristics, an insufficient focus on renewable energy and storage systems, a bias in favor of residential buildings and a preference for real-time pricing schemes. The main conclusions are related to the promising hybridization between the genetic algorithms and swarm optimization approaches, as well as a greater integration of user preferences in the optimization. Moreover, there is a need for accurate renewable and storage models, as well as for broadening the optimization scope to other objectives such as CO₂ emissions or communications load.

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.

This article highlights the development of a platform for monitoring three-phase energy consumption within a university campus. The core of this platform is low-cost IoT energy sensors, which are designed to transmit real-time data to the data center’s server through different IoT communication technologies, enhancing the preexisting electrical measurement network. The newly recommended measurement structure enables the electrical consumption data collection required for analyzing patterns and proposing forecast models to optimize electricity usage. The major contribution of this work is the design and implementation of smart three-phase energy meters based on the selection of various energy sensors and wireless communication technologies, and then the set up of a global IoT architecture that offers real-time data acquisition, storage, download, and visualization, capitalizing on the campus’s diverse energy profiles for detailed characterization. The proposed platform is considered the cornerstone toward the implementation of a collaborative smart microgrid, allowing forecasting and electrical consumption optimization, enabling research into potential opportunities for energy efficiency in our campus, and enhancing the performance of existing electrical infrastructure.

In this paper, we propose a visually optimized embedded foveation image coder thereafter called VOEFIC. It introduces a visual weighting model to wavelet coefficients prior to control SPIHT encoding algorithm. It aims reach a targeted bit rate with a perceptual quality improvement with respect to a given bit rate, observation distance and a fixation point that determines the region of interest ROI. Our VOEFIC coder incorporates a battery of vision models including various masking effects related to the human visual system (HVS) perception. Thus, the coder deploys the visual model to weight the original wavelet coefficients, reshape its frequency spectrum, adapts its contents, eliminates its redundancy and finally increases the perceptual quality with respect to given coding parameters. The visual weighting model within wavelet sub-bands uses successively the following visual models 1) foveation masking applied to remove or at least to reduce high frequencies around peripheral regions 2) Luminance and Contrast masking deployed to adapt lightening and correct the contrast with to the wavelet just noticeable distortion thresholds. The paper also introduces a new objective quality metric deploying a visual model that integrates the human visual properties. The visual quality assessor offers a score based on a foveation probability scale FPS evaluate objectively the coding quality. The new perceptually optimized codec has the same complexity as the standard SPIHT coder. However, its visual results show that VOEFIC coder exhibits very good visual performances in terms of quality assessment index.