Location prediction approach for context-aware energy management system

Shaptala, Roman; Kyselova, Anna

doi:10.1109/elnano.2016.7493079

Cited by 3 publications

(1 citation statement)

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“…Normalization: This step ensures data uniformity by scaling electricity prices and loading demand data to a common reference point, typically the mean or median. The normalization process promotes equitable contributions of features to model training and prediction, irrespective of their scale or magnitude [53].…”

Section: Data Collection and Preprocessingmentioning

confidence: 99%

A Scheduling Algorithm for Appliance Energy Consumption Optimization in a Dynamic Pricing Environment

Touhs,

Temouden,

Khallaayoun

et al. 2023

WEVJ

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This research delves into the intricate landscape of energy scheduling and optimization within microgrid and residential contexts, addressing pivotal aspects such as real-time scheduling systems, challenges in dynamic pricing, and an array of optimization strategies. This paper introduces a cutting-edge scheduling algorithm, harnessing the power of artificial neural networks driven by Long Short-Term Memory Networks, and highlights its exceptional performance, boasting a significantly lower Mean Absolute Error of 5.32 compared to conventional models. This heightened predictive accuracy translates into tangible improvements in both energy efficiency and cost savings. This study underscores the delicate balance between user satisfaction, cost reduction, and efficient scheduling for sustainable energy consumption, showcasing a remarkable 38% enhancement in optimized schedules. Further granularity revealed substantial gains in energy efficiency and cost reduction across different scheduling intensities: 11.11% in light schedules, 20.09% in medium schedules, and an impressive 38.85% in heavy schedules. However, this research does not shy away from highlighting challenges related to data quality, computational demands, and generalizability. Future research trajectories encompass the development of adaptive models tailored to diverse data qualities, enhancements in scalability for and adaptability to various microgrid configurations, the integration of real-time data, the accommodation of user preferences, the exploration of energy storage and renewables, and an imperative focus on enhancing algorithm transparency.

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Section: Data Collection and Preprocessingmentioning

confidence: 99%