Neighborhood Energy Modeling and Monitoring: A Case Study

Abstract

Cities and nations worldwide are pledging to energy and carbon neutral objectives that imply a huge contribution from buildings. High-performance targets, either zero energy or zero carbon, are typically difficult to be reached by single buildings, but groups of properly-managed buildings might reach these ambitious goals. For this purpose we need tools and experiences to model, monitor, manage and optimize buildings and their neighborhood-level systems. The paper describes the activities pursued for the deployment of an advanced energy management system for a multi-carrier energy grid of an existing neighborhood in the area of Milan. The activities included: (i) development of a detailed monitoring plan, (ii) deployment of the monitoring plan, (iii) development of a virtual model of the neighborhood and simulation of the energy performance. Comparisons against early-stage energy monitoring data proved promising and the generation system showed high efficiency (EER equal to 5.84), to be further exploited.

UBEM's archetypes improvement via data-driven occupant-related schedules randomly distributed and their impact assessment

“…A complete validation of this model is relatively useless for this scenario since the focus is on the contrast among results of different scenarios, more than on the value of the energy results. However, the values have been compared with North Italian benchmarks present in the literature, which show to be consistent [64,65]. Figure 7 shows the daily results in terms of daily energy needs (kWh) for the neighbourhood.…”

Opaque Coloured Building Integrated Photovoltaic (BIPV): A Review of Models and Simulation Frameworks for Performance Optimisation

“…The twofold purpose of this technology can unlock the solar potential of a large set of vertical and horizontal envelope surfaces typically not exploited, harvesting solar energy for on-site energy production and leading the building stock to energy flexibility and self-sufficiency [1,2]. These aspects are particularly relevant in densely built environments since they promote equal access to renewable energy production for users living in urban areas, where several constraints hinder the exploitation of solar energy, i.e., where traditional ground-mounted photovoltaic (PV) installations are not possible and the solar potential for roof-mounted PVs is low compared to the multi-property and multi-story-building energy demand [3][4][5]. Furthermore, exploiting building surfaces for solar energy harvesting can contribute to meeting the targets for renewable energy penetration in a country's energy mix (as in the case of European member states), while reducing the need for the allocation of land to solar farms [6].…”

Application of Machine Learning Models for Fast and Accurate Predictions of Building Energy Need

“…Among the most relevant ones, we find building energy simulations, characterizing different scales [4] and in different locations [5,6]. Computer programs like EnergyPlus [7] simulate different building configurations, returning precise and accurate results that take into account all variables affecting building energy consumption for the indoor climate control (e.g., envelope materials, orientation, systems, weather data).…”