Prognosis of heat consumption of smart grid buildings on basis of the weather forecast: Investigation of buildings' heat capacity and design of a calculation tool on basis of EN ISO 13790

Orlikowski, Jacqueline Valencienne

4. term

Indoor Environmental and Energy Engineering, Master

2014

2014-06-06

71

Dette speciale undersøger bygningers evne til at lagre varme (termisk masse) og udvikler et beregningsværktøj, der kan forudsige varmebehovet efter den simple timebaserede metode i EN ISO 13790. Baggrunden er smart grid-løsninger, hvor solceller (PV) og varmepumper arbejder sammen. Fordi solceller kun leverer strøm i en begrænset del af døgnet, er det en fordel, at bygninger kan lagre varme og holde en behagelig indetemperatur, når varmen ikke tilføres. Der er opstillet otte bygningsvarianter med forskellig “tyngde” (fra ekstra let til ekstra tung) som udtryk for varmekapacitet, og med to isoleringsniveauer: almindelig nutidig standard og passivhusstandard. Med BSim er hvert hus simuleret med 8 timers opvarmning pr. døgn, hvorefter temperaturen følges i de efterfølgende 16 timer uden opvarmning for at se, hvor godt komforten holdes. Resultaterne viser, at smart grid-drift er fornuftig i bygninger efter nutidig standard, især når de indeholder tunge materialer. Alle passivhusvarianter, uanset om de er lette eller tunge, er egnede til smart grid-implementering, fordi deres lave varmetab hjælper med at holde på varmen. Specialet præsenterer også et enkelt beregningsværktøj, der kan integreres i en varmepumpes styring for at forudsige varmebehov time for time. Det bygger på EN ISO 13790 (simpel time-metode), som kan bruge timebaserede målinger og kræver få inputdata. En forenklet følsomhedsanalyse af vejrfaktorer (udendørstemperatur, solindstråling, vind og relativ luftfugtighed) afgrænser de nødvendige input, som kan hentes fra vejrtjenester. Derudover beskrives nødvendige oplysninger om bygning og ventilation samt håndteringen af sol- og interne varmetilskud. Værktøjets output er forventet varmebehov og forventet indetemperatur, når opvarmning kun sker i et begrænset tidsrum. Værktøjet er valideret ved at sammenholde simulationer med målinger fra et Comfort House-projekt. Dels sammenlignes simulerede operative temperaturer (et komfortmål for indetemperatur) med målte værdier ved kendt varmebehov, dels sammenlignes simuleret varmebehov med målt varmetilførsel ved en fastlagt setpunktstemperatur. Afvigelserne ligger inden for acceptable grænser, hvilket peger på, at værktøjet kan bruges i varmepumpestyringer.

This thesis examines how well buildings can store heat (thermal mass) and develops a calculation tool that predicts heating demand using the EN ISO 13790 simple hourly method. The context is smart grid operation, where photovoltaics (PV) and heat pumps work together. Because PV power is only available for part of the day, buildings benefit from being able to store heat and maintain comfortable indoor temperatures when heating is not actively supplied. Eight building variants were created with different “heaviness” (from extra light to extra heavy) to represent varying heat capacity, and with two insulation levels: typical current standard and Passive House standard. Using BSim, each building was simulated with 8 hours of heating per day, followed by 16 hours without heating, to see how well comfort is maintained. The results indicate that smart grid operation is sensible in current-standard buildings, especially those with heavy materials. All Passive House variants, from light to heavy, are suitable for smart grid implementation because their low heat loss helps retain warmth. The thesis also presents a simple calculation tool that can be integrated into a heat pump controller to forecast hourly heating demand. It is based on EN ISO 13790 (simple hourly method), which can use hourly measurements and requires limited input data. A simplified sensitivity analysis of weather inputs (outdoor air temperature, solar radiation, wind, and relative humidity) narrows the necessary data to what is available from weather services. Required building and ventilation inputs are described, and solar and internal heat gains are accounted for. The tool outputs expected heating demand and indoor air temperature when heating is supplied only during a limited time window. The tool was validated by comparing simulations with measurements from a Comfort House project building. First, simulated operative temperatures (a comfort-focused indoor temperature measure) with a given heat demand were compared to measured operative temperatures. Second, simulated heating demand with a given setpoint was compared to measured heating supply. Deviations were within an acceptable range, supporting the tool’s use in heat pump controllers.

[This abstract was generated with the help of AI]

smart grid ; heat consumption ; heat capacity

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