The Study of Challenge and Issue of Building Demand Response

Noppakant, Akeratana; Plangklang, Boonyang; Marsong, Supapradit

doi:10.1109/icpei47862.2019.8945005

Cited by 4 publications

(1 citation statement)

References 3 publications

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“…The reduction of energy cost and energy life extent are two important points studied in the context of smart grids [4]. These aspects are within the context of Demand Response (DR), which is related to the reduction of electricity consumption in particular periods in order to decrease the electricity costs [5,6].…”

Section: Introductionmentioning

confidence: 99%

Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental Learning

et al. 2020

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Society’s concerns with electricity consumption have motivated researchers to improve on the way that energy consumption management is done. The reduction of energy consumption and the optimization of energy management are, therefore, two major aspects to be considered. Additionally, load forecast provides relevant information with the support of historical data allowing an enhanced energy management, allowing energy costs reduction. In this paper, the proposed consumption forecast methodology uses an Artificial Neural Network (ANN) and incremental learning to increase the forecast accuracy. The ANN is retrained daily, providing an updated forecasting model. The case study uses 16 months of data, split in 5-min periods, from a real industrial facility. The advantages of using the proposed method are illustrated with the numerical results.

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Section: Introductionmentioning

confidence: 99%