Forecasting peak energy demand for smart buildings
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Forecasting peak energy demand for smart buildings Mona A. Alduailij1 · Ioan Petri2 · Omer Rana3 · Mai A. Alduailij1 · Abdulrahman S. Aldawood4 Accepted: 24 November 2020 © The Author(s) 2020
Abstract Predicting energy consumption in buildings plays an important part in the process of digital transformation of the built environment, and for understanding the potential for energy savings. This also contributes to reducing the impact of climate change, where buildings need to increase their adaptability and resilience while reducing energy consumption and maintain user comfort. The use of Internet of Things devices for monitoring and control of energy consumption in buildings can take into account user preferences, event monitoring and building optimization. Detecting peak energy demand from historical building data can enable users to manage their energy use more efficiently, while also enabling real-time response strategies (including control and actuation) to known or future scenarios. Several statistical, time series, and machine learning techniques are proposed in this work to predict electricity consumption for five different building types, by using peak demand forecasting to achieve energy efficiency. We have used several indigenous and exogenous variables with a view to test different energy forecasting scenarios. The suggested techniques are evaluated for creating predictive models, including linear Regression, dynamic regression, ARIMA time series, exponential smoothing time series, artificial neural network, and deep neural network. We conduct the analysis on an energy consumption dataset of five buildings from 2014 until 2019. Our results show that for a day ahead prediction, the ARIMA model outperforms the other approaches with an accuracy of 98.91% when executed over a 168 h (1 week) of uninterrupted data for five government buildings. Keywords Energy forecasting · Time series · ARIMA · Peak demand · ANN · Smart buildings
* Ioan Petri [email protected] Extended author information available on the last page of the article
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1 Introduction Climate change strategies were introduced in 2010 by the European Commission with clear objectives to reduce energy consumption and CO2 emissions by 20%, noting that in Europe 40% of total energy is consumed by buildings (Directive 2010/31/EU) [1]. With the introduction of Smart Building Readiness Level, buildings are expected to “minimize the grid power usage and maximize services efficiency” identifying components such as sensors, renewable energy sources, and energy management system (EMS) [2]. Smart built environments have gone through a continuous transformation over the years, becoming more autonomous and reactive ecosystems that have the ability to balance energy consumption and user comfort, whilst also achieving higher order of safety for users [3]. Minimizing the energy consumption of buildings also has a cost dimension, as energy prices are fluctuating, which gives energy consumers and providers the a
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