An Insight of Deep Learning Based Demand Forecasting in Smart Grids

Abstract

Smart grids are able to forecast customers’ consumption patterns, i.e., their energy demand, and consequently electricity can be transmitted after taking into account the expected demand. To face today’s demand forecasting challenges, where the data generated by smart grids is huge, modern data-driven techniques need to be used. In this scenario, Deep Learning models are a good alternative to learn patterns from customer data and then forecast demand for different forecasting horizons. Among the commonly used Artificial Neural Networks, Long Short-Term Memory networks—based on Recurrent Neural Networks—are playing a prominent role. This paper provides an insight into the importance of the demand forecasting issue, and other related factors, in the context of smart grids, and collects some experiences of the use of Deep Learning techniques, for demand forecasting purposes. To have an efficient power system, a balance between supply and demand is necessary. Therefore, industry stakeholders and researchers should make a special effort in load forecasting, especially in the short term, which is critical for demand response.

Advanced Integration of Forecasting Models for Sustainable Load Prediction in Large-Scale Power Systems

“…This approach aligns with the advancements in the field, as highlighted by studies such as Aguiar et al [21] and Sharma et al [22], which emphasize the importance of integrating various forecasting methods to enhance prediction accuracy in large-scale power systems.…”

“…In conclusion, the factors influencing regional power load primarily include economic, demographic, natural, and occasional factors. However, natural factors, which only impact short-term maximum power loads or cause seasonal fluctuations, have minimal influence on long-term planning and are thus less considered in this study [21]. In contrast, economic, demographic, and occasional factors significantly impact power load and are crucial in developing an integrated forecasting model using advanced techniques such as Causal CNN and VAE.…”

Environmental Impact on RF and PLC for Advanced Metering Infrastructure in Smart Grids

“…Neural grid represents a hypothetical concept and a theoretical extension of smart grids technologies, incorporating machine learning capabilities and advanced artificial intelligence [1]. Its main objectives are sustainable energy integration, advanced energy management capabilities for consumers, energy data security and real-time data prediction.…”

The Environmental Impacts of Radio Frequency and Power Line Communication for Advanced Metering Infrastructures in Smart Grids

“…A neutral grid represents a hypothetical concept and a theoretical extension of smart grid technologies, incorporating machine learning capabilities and advanced artificial intelligence [ 1 ]. Its main objectives are sustainable energy integration, advanced energy management capabilities for consumers, energy data security, and real-time data prediction.…”

“…This paper focuses on investigating the communication between the data concentrator (DC) and the smart meter (SM) in three selected areas: rural, urban, and industrial. To address this objective, the authors engage in standardization efforts, considering two solutions: (1) incorporating functioning standards into the existing electricity grid with necessary modifications, and (2) developing new communication protocols tailored to the communication requirements of the smart grid.…”