Hourly energy profile determination technique from monthly energy bills
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Hourly energy profile determination technique from monthly energy bills
1. Department of Astronautical, Electrical and Energy Engineering (DIAEE), Sapienza University of Rome, Via Eudossiana, 18 - 00184 Rome, Italy 2. Department of Planning, Design, Technology of Architecture, Sapienza University of Rome, Via Flaminia 72 – 00196 Rome, Italy
Abstract
Keywords
Hourly energy consumption profiles are of primary interest for measures to apply to the dynamics of the energy system. Indeed, during the planning phase, the required data availability and their quality is essential for a successful scenarios’ projection. As a matter of fact, the resolution of available data is not the requested one, especially in the field of their hourly distribution when the objective function is the production-demand matching for effective renewables integration. To fill this gap, there are several data analysis techniques but most of them require strong statistical skills and proper size of the original database. Referring to the built environment data, the monthly energy bills are the most common and easy to find source of data. This is why the authors in this paper propose, test and validate an expeditious mathematical method to extract the building energy demand on an hourly basis. A benchmark hourly profile is considered for a specific type of building, in this case an office one. The benchmark profile is used to normalize the consumption extracted from the 3 tariffs the bill is divided into, accounting for weekdays, Saturdays and Sundays. The calibration is carried out together with a sensitivity analysis of on-site solar electricity production. The method gives a predicted result with an average 25% MAPE and a 32% cvRMSE during one year of hourly profile reconstruction when compared with the measured data given by the Distributor System Operator (DSO).
building load profile,
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Introduction
E-mail: [email protected]
building energy demand model, simplified hourly distribution, simulated hourly demand
Article History Received: 19 May 2020 Revised: 08 July 2020 Accepted: 24 July 2020 © Author(s) 2020
with the performance gap between design and operation of buildings (Manfren and Nastasi 2020). Smart energy approach is, then, required to link different production and consumption nodes (Rosenbloom and Meadowcroft 2014) keeping in mind that automation and communication in models and reality is fundamental for its success (Tronchin et al. 2018). Energy Management Systems (EMS) are keen in it, composed of hardware and software for optimal control and rational use of energy (Li et al. 2019) enabling strategy as demand-response. For the aforementioned systems, availability and quality of the information are essentials for their effective outcome (Erdinc and Uzunoglu 2011). Moreover, even in the design phase for any intervention to improve the building performance such as lowering energy consumption or including it in a micro-grid, the data on hourly resolution are of primary interest. There are two possibilities to obtai
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