Proactive control for solar energy exploitation: A german high-inertia building case study

Abstract

Abstract-Energy efficient passive designs and constructions have been extensively studied in the last decades as a way to 2 improve the ability of a building to store thermal energy, increase its thermal mass, increase passive insulation and 3 reduce heat losses. However, many studies show that passive thermal designs alone are not enough to fully exploit the 4 potential for energy efficiency in buildings: in fact, harmonizing the active elements for indoor thermal comfort with the 5 passive design of the building can lead to further improvements in both energy efficiency and comfort. These 6 improvements can be achieved via the design of appropriate Building Optimization and Control (BOC) systems, a task 7 which is more complex in high-inertia buildings than in conventional ones. This is because high thermal mass implies a 8 high memory, so that wrong control decisions will have negative repercussions over long time horizons. The design of 9 proactive control strategies with the capability of acting in advance of a future situation, rather than just reacting to 10 current conditions, is of crucial importance for a full exploitation of the capabilities of a high-inertia building. This paper 11 applies a simulation-assisted control methodology to a high-inertia building in Kassel, Germany. A simulation model of 12 the building is used to proactively optimize, using both current and future information about the external weather 13 I. INTRODUCTION 20Motivated by the fact that around half of the energy produced on the planet is used for the daily needs of building systems, 21 and floors are made to store, cage and distribute solar energy in the form of heat in the winter and reject solar heat in the 26 summer; passive cooling with different forms of ventilation and earth coupling; passive day-lighting to most effectively capture 27

Sustainability Performance Evaluation of Hybrid Energy System Using an Improved Fuzzy Synthetic Evaluation Approach

“…• Power supply reliability (C22) [37]: where ∆W E,T , ∆W H,T , and ∆W C,T represent the deviation of electric supply, heat supply, and cool supply at the T period, respectively; P L,E (t), P L,H (t), and P L,C (t) indicate the demand of users for electric load, heat load, and cooling load at the T period, respectively; P o,i (t) is the actual output power for i renewable energy units at the T period; P E,C (t) is the electric refrigerating at the T period; P CCHP,E (t), P CCHP,H (t), and P CCHP,C (t) are the actual power output of CCHP with electricity, heat, and cold at the T period, respectively; and P S,k (t) is the discharge power of k energy storage device at the T period. Any reader interested in the energy demand prediction aspect of an HES can refer to [38][39][40].…”

Economic Optimal HVAC Design for Hybrid GEOTABS Buildings and CO2 Emissions Analysis

“…[16][17][18]. While this control aspect is beyond the scope of the paper, readers interested in (optimal) control of GEOTABS buildings are referred to [12,[19][20][21][22][23] for rule-based controllers and to [24][25][26][27][28][29][30][31]) for optimal control.…”

Energetic, Cost, and Comfort Performance of a Nearly-Zero Energy Building Including Rule-Based Control of Four Sources of Energy Flexibility

“…They emphasize that energy sharing between buildings can reduce costs and show that their optimized method outperforms rule-based DR strategies by up to 25%. Similarly, Michailidis et al [9] also implemented PCAO with current and future information about weather conditions and building state to manage a low energy, high-inertia building, resulting iñ 50% energy savings. In contrast, Neves et al [10] found existing modeling tools that offered savings below 0.5% in a case study of the island of Corvo, Portugal, partly due to the input data limitations.…”