Use Case: Thermal Model #340
Replies: 18 comments 15 replies
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This is my EMHASS at the moment. I have PV, Batteries, Nordpool electricity prices. Just yesterday made the EV charging controll to emhass as well. I would like to use the thermal model to control my heat pump which does under floor water heating and hot water heating. I have a Novelan heat pump, which is basically Nibe. It's connected via modbus to HA and i can tweak pretty much everything. As for floor heating, I'm feeding in my room temperature and can play with heating offset. When i lower the offset i can tell heat pump to stop working and raising it to work. I use this logic at the moment to optimize usage on the lowest electricity prices and stop it when there are high peaks. My house is ne, pretty well insulateted and floor heating has good inertia, so even during -20 degrees (yes, i'm living in the north) house temperature does not drop more than 1 degrees in one day. So it's good starting point for using optimization. of course heating up more takes more energy, so it could be that it's not optimize to let the house cool down much. meaning that if the heatpump has to work on higher frequency it takes a lot more electricity than working steadily on a lower frequency. Fortunatelly i have the power meter on the heat pump and calculate CoP from this and heat pump heating energy numbers. The fact that power consumption depends on the frequency of the compressor makes it harder to predict it. Frequency is something that heat pump is calculating itself from the degree minutes, outside temp etc. There is probably a way to set it manually as well, but this seems already too complicated to calculate. As for hot water heating it has three settings (economy, normal, lux). Each of them have start and stop temperatures which can be setup. For example economy starts heating water in the boiler (200l) when temp is 39 degrees and stops at 43 degrees. Normal 42-46 and lux 45-49. I have used it so that when electricity price is high its economy and when veeery low then lux. I also have circulation pump and turn it off when nobody is at home and at night. For example in economy mode when circulation pump is on, it takes 4h for water to cool from 43 to 39 and when off it takes about 10 hours. So i can skip at least one heating cycle. For hot water complication is that it depends heavily on the actual hot water use, which can be tricky to predict. On the other hand it might not, because when family is at home its usually more on the mornings and evenings. And last - i have also AC in my house. In the summer i have to cool my house and this can be also controlled with the thermal model. I can control it from HA as well. So i think i have to define three two thermal models - heating, hot water and AC. At the moment there is no need for heating yet, so i can play with it, but i can start with AC and hot water. But before I continue, I need thermal model support longer period than 24h, as my current MPC is dynamic and prediction period is as long as i have nordpool data. Usually max 34h. |
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Now i have looked more in the Thermal Model. Key is to know heating_rate and cooling_constant. I saw that EMHASS has also The machine learning regressor and looks like this could be something to use to collect data in the model and predict the heating_rate and cooling_constant. So i would collect this daily data from HA to CSV:
Now when writing this I came to a conclusion that this way I cannot get the heating_rate and cooling_constant, as these are daily values and resolution is not enough, but what i would get is prediction of next 24h heating hours and power needed, which I can feed in to deferrable load. On the other hand, if I would increase data collection resolution to 1h then i should be able to calculate heating_rate and cooling_constant as well. @davidusb-geek @werdnum , what are your thoughts? Maybe i could feed in my heating power sensor instead? From it both power and heating hours can be hourly (or even more frequent) data collected and correlated with the outside and inside temperature. |
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Hi. Yes definitely, the heating rate and cooling constant are good candidates to be predicted by the ML regressor method. However when you have your data with the correct resolution start by computing these directly over a small period and use them to set and get initial results for the thermal modeling. Then if satisfied with the results go ahead and try the ML method to predict them automatically |
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what would be a good resolution to start? Hourly? At the moment i'm collecting daily data to csv: these are mean average values of the day And what I already have. Note that it's not heating season yet: |
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@davidusb-geek , could you guide me to the right direction of initial data collection. What would be a reasonable resolution? Hourly? I actually should have at least 7 days of data stored in HA which i can use, but i have no idea how to get it out from HA. |
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Yes I think that hourly data should be more than enough for a thermal model. |
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So i made a python script that will take 21 days of outdoor and indoor temp data from HA API. Then made script that will filter out
Then used the cooling constant calculation After that IQR outlier detection and Isolation Forest for anomaly detection to remove anomalies And now i should have a cooling constant. At the moment i'ts good to measure, because i'n not heating yet. Just thinking, that it does not make sense to calculate it when i turn heating on, as then the calculation goes wrong. So to my understanding i now have a cooling constant which is part of the equation. Next step would be be to calculate the heating rate when i turn on heating. Filtered Data after IQR and Isolation Forest: Cleaned Average Cooling Constant (Mean): 0.011841 I think using median as my cooling constant makes more sense than using the average, which still has some spikes regarding using the IQR and isolation forest. |
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Describing my ad-hoc thermal model. I want to optimise for cooling as we are heading towards summer, this is a simple use case as the hottest part of the day also has the greatest solar pv production and thus the cheapest solution for EMHASS to optimise against: You can see my HVAC power forecast in blue (11 kW and 5 hours): I set def_total_hours_hvac = the number of hours (5) the forecast is at or above my desired setpoint (24 °C) I look at the number of hours( 5 hours >= 24 °C) the external temp is forecast to be above a desired setpoint: Then my HVAC automation uses the cooling factor of 1500 W / °C (which I have measured in my house): If the external temp is 26 °C and deferrable3 is forecasting 3000W, then my HVAC setpoint is 24 °C (2 °C below external) This seems to give me something reasonable, where the HVAC load follows the demand identified by deferrable3 and factors both external temp, cost and a controllable setpoint. I do observe when the cost goes up, the HVAC consumption is reduced, when the price goes down HVAC consumption is increased
I'm not a HVAC engineer so a lot of this has been developed through trial and error. In my next post, I'll talk about how I'm using the EMHASS thermal model to try and produce a similar outcome. |
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When applying the EMHAS thermal model to the same climate forecasts I use the folllowing payloads: Which processes correctly, but I end up with a very bizzare deferrable3 forecast (the blue chart), which I can't explain and I'm wondering which settings of the EMHASS thermal model require modification: After some thought, this HVAC power forecast looks like the heating required for 24° C, so the question is how to switch to cooling?
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@werdnum does the EMHASS thermal model only currently support heating? I'm heading into summer and am looking to implement for cooling, but couldn't find any flags to switch over. I would image the model could support cooling, but is currently hard coded for heating only. |
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I forget the details but pretty sure it can do both. Either there's a sense
parameter and/or you set the heating rate negative.
— Andrew Garrett
…On Sun, 29 Sep 2024 at 16:32, Mark Purcell ***@***.***> wrote:
@werdnum <https://github.com/werdnum> does the EMHASS thermal model only
currently support heating?
I'm heading into summer and am looking to implement for cooling, but
couldn't find any flags to switch over.
I would image the model could support cooling, but is currently hard coded
for heating only.
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Yeah set sense to cool and heating rate negative:
https://github.com/davidusb-geek/emhass/blob/master/src/emhass/optimization.py#L417
— Andrew Garrett
…On Sun, 29 Sep 2024 at 16:45, Andrew Garrett ***@***.***> wrote:
I forget the details but pretty sure it can do both. Either there's a
sense parameter and/or you set the heating rate negative.
— Andrew Garrett
On Sun, 29 Sep 2024 at 16:32, Mark Purcell ***@***.***>
wrote:
> @werdnum <https://github.com/werdnum> does the EMHASS thermal model only
> currently support heating?
>
> I'm heading into summer and am looking to implement for cooling, but
> couldn't find any flags to switch over.
>
> I would image the model could support cooling, but is currently hard
> coded for heating only.
>
> —
> Reply to this email directly, view it on GitHub
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Nice work @werdnum, cooling profile looks good too.
Oddly I have lost the scheduling of my other deferrable loads. |
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@werdnum , i'm preparing the thermal model as well to start controlling and optimimizing my heating. I thing I see current model does not take account is Coefficient of Performance (COP), which significantly affects the relationship between the electrical energy consumed and the actual heat energy delivered. The COP depends on outdoor temperature and directly influences how much energy the heat pump outputs relative to the electricity it consumes. When the COP is involved, the amount of heating power delivered is not simply the electrical power consumed by the heat pump but rather: With warm outside temp like +10 C, COP can be 4 or even more, but -20C it's 1, so there is heat energy equals electricity energy. I understand that the model currently has heating energy and it's assumed to be equal to electricity energy. I would suggest to add the COP to the equation, but it's quite individual to different heating elements and probably not linear, which makes it complicated to implement. |
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The thermal model uses the heating constant as a crude conversion factor
between electrical power used and change in temperature. It's defined as
the amount the temperature will go up if the heating runs at full blast for
an hour, neglecting losses to the outside which are modelled using the
cooling constant.
There are too many variables to attempt a conversation from first
principles between power input and temperature rise. The COP is one, the
air volume of the conditioned space is another, as is thermal mass and so
on. You can capture all of those effects with a single constant by
observing (I basically just eyeballed it from a graph) the temperature rise
from running your system at full blast for an hour. My number is 3-5°C but
yours will probably be different.
— Andrew Garrett
…On Tue, 1 Oct 2024 at 20:14, martinarva ***@***.***> wrote:
@werdnum <https://github.com/werdnum> , i'm preparing the thermal model
as well to start controlling and optimimizing my heating.
I thing I see current model does not take account is Coefficient of
Performance (COP), which significantly affects the relationship between the
electrical energy consumed and the actual heat energy delivered. The COP
depends on outdoor temperature and directly influences how much energy the
heat pump outputs relative to the electricity it consumes.
When the COP is involved, the amount of heating power delivered is not
simply the electrical power consumed by the heat pump but rather:
P_{\text{heat}} = P_{\text{elec}} \cdot \text{COP}(T_{\text{out}})
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My number is probably something between 1-2. I have under floor heating has quite a big inertia. If I heat for an hour, heat "arrives" probably after few hours and last longer. Probably have to fiddle with the settings and see how it acts when heating season really starts. It should happen in upcoming weeks. |
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So do i understand correctly that I should have my own logic for the heating constant that will take account of the CoP outside temp correlation etc? |
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Hi all, thanks for the info in this thread and work to get the thermal model implemented - we just got a heat pump installed so very interested in using this for it. I'm having an issue where I can only return infeasible, even though it seems like the model has returned appropriate values (except for my other deferable load only showing 1w). There's nothing in the log to suggest why it's infeasible. I wondered if it was the hours I set for def2, but I upped that and it didn't seem to change anything. Can you see if I've missed something obvious? Payload to Emhass: |
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I find I have feasibility issues quite often. I think one common cause is
setting a desired temperature for the first or second timestep - if that
temperature isn't being met right now, then the whole thing is infeasible
because the system has no time to meet it.
I think a better solution would be to replace the constraints with
penalties for not meeting the desired temperature, but I haven't got around
to it (I have half a change somewhere in my local repo I think)
…On Thu, Oct 3, 2024 at 1:28 AM Stouten80 ***@***.***> wrote:
Hi all, thanks for the info in this thread and work to get the thermal
model implemented - we just got a heat pump installed so very interested in
using this for it. I'm having an issue where I can only return infeasible,
even though it seems like the model has returned appropriate values (except
for my other deferable load only showing 1w). There's nothing in the log to
suggest why it's infeasible. I wondered if it was the hours I set for def2,
but I upped that and it didn't seem to change anything. Can you see if I've
missed something obvious?
Payload to Emhass:
{ "soc_final": 0.5, "soc_init": 0.89, "load_cost_forecast": [ 0.27258,
0.32529, 0.33327, 0.36708, 0.366765, 0.38094, 0.23331, 0.227115, 0.20832,
0.160965, 0.18564, 0.18522, 0.169155, 0.165795, 0.17661, 0.167475,
0.160965, 0.16317, 0.165585, 0.177765, 0.172095, 0.1701, 0.16695, 0.169785,
0.185325, 0.17682, 0.17745, 0.1701, 0.21168, 0.20958, 0.24024, 0.255465,
0.24801, 0.24801, 0.240345, 0.223335, 0.204225, 0.18123, 0.169575,
0.169575, 0.17199, 0.17199, 0.165375, 0.165375, 0.1701, 0.17199, 0.17199,
0.17661 ], "pv_power_forecast": [ 214, 180, 170, 97, 33, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 701, 1221,
1653, 2035, 2381, 2622, 2873, 2950, 2916, 2851, 2671, 2424, 2049, 1726,
1399, 1078, 718 ], "load_power_forecast": [ 500, 500, 800, 1000, 2500,
2500, 400, 400, 400, 400, 300, 300, 300, 300, 200, 200, 200, 200, 200, 200,
200, 200, 200, 200, 200, 200, 200, 200, 200, 400, 400, 400, 600, 600, 500,
500, 500, 500, 500, 500, 400, 400, 400, 400, 400, 400, 400, 500 ],
"prediction_horizon": 48, "def_total_hours": [5, 2, 5], "set_def_constant":
[false, true, false], "def_current_state": [0, 0, 0], "def_start_penalty":
[1, 5.5, 1], "P_deferrable_nom": [1, 2000, 2000], "def_start_timestep": [0,
13, 0], "def_end_timestep": [0, 28, 0], "P_from_grid_max": [22000],
"SOCmin": 0.11, "SOCmax": 0.9, "outdoor_temperature_forecast": [ 14.7,
14.7, 14.1, 14.1, 13.2, 13.2, 12.4, 12.4, 11.5, 11.5, 10.9, 10.9, 10.4,
10.4, 10, 10, 9.7, 9.7, 9.5, 9.5, 9.1, 9.1, 8.7, 8.7, 8.3, 8.3, 7.9, 7.9,
7.7, 7.7, 7.9, 7.9, 9.5, 9.5, 11.1, 11.1, 12.7, 12.7, 14.1, 14.1, 15.3,
15.3, 15.7, 15.7, 15.9, 15.9, 15.1, 14.7 ], "def_load_config": [ {}, {}, {
"thermal_config": { "heating_rate": 5, "cooling_constant": 0.1,
"overshoot_temperature": 23, "start_temperature": 20.8,
"desired_temperatures": [ 18, 18, 18, 18, 18, 21, 21, 21, 21, 21, 21, 21,
19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 21, 21, 21, 21,
20, 20, 20, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 22, 22, 18 ] } } ]
}
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— Andrew Garrett
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Thanks! Hmm it makes sense that that would be infeasible, but an overshoot, so long as lower than the overshoot temp set should be fine no? Current desired temp is 18, but it's 20.8 in the house. Been trying to get it to work a bunch of times over the last few days and don't think I ever got anything but infeasible, so thought it must be something I'm doing wrong (and the def1 load being 1w, rather than 2000) |
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It would be great if we could get some more debug logging with the thermal model so we can see what is going on under the covers. |
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I find the overshoot_temperature also causes infeasible solutions. I had overshoot set to 16 deg (I'm running with cooling), but the overnight temp were going below 16 deg, and that was infeasible. I have now set overshoot_temperature to less the the minimum overnight temp and I'm back to a feasible solution. 2024-10-04 22:53:00,271 - web_server - INFO - Passed runtime parameters: {'load_cost_forecast': [0.23, 0.2, 0.19, 0.18, 0.18, 0.18, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.16, 0.12, 0.09, 0.07, 0.07, 0.07, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.05, 0.06, 0.08, 0.25, 0.3, 0.34, 0.36, 0.37, 0.36, 0.35, 0.34, 0.34, 0.34, 0.21, 0.2, 0.2, 0.2], 'prod_price_forecast': [0.1, 0.08, 0.07, 0.07, 0.06, 0.06, 0.06, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.04, 0.01, -0.02, -0.04, -0.04, -0.04, -0.04, -0.04, -0.04, -0.05, -0.04, -0.04, -0.04, -0.04, -0.04, -0.04, -0.04, -0.04, -0.03, -0.01, 0.01, 0.06, 0.09, 0.11, 0.12, 0.11, 0.1, 0.09, 0.09, 0.09, 0.09, 0.08, 0.08, 0.08], 'load_power_forecast': [531, 300, 300, 300, 300, 300, 300, 700, 700, 1200, 400, 400, 300, 300, 300, 300, 800, 1000, 1000, 2000, 1000, 500, 500, 500, 600, 600, 3100, 3100, 300, 300, 300, 300, 300, 300, 300, 300, 600, 700, 700, 1700, 1200, 800, 600, 500, 400, 500, 600, 700, 300, 300, 300, 300, 300, 300, 300, 700, 700, 1200, 400, 400, 300, 300, 300, 300, 800, 1000, 1000, 2000, 1000, 500, 500, 500, 600, 600, 3100, 3100, 300, 300, 300, 300, 300, 300, 300, 300, 600, 700, 700, 1700, 1200, 800, 600, 500, 400, 500, 600, 700], 'prediction_horizon': 48, 'def_load_config': [{}, {}, {}, {'thermal_config': {'heating_rate': -5.0, 'sense': 'cool', 'cooling_constant': 0.2, 'overshoot_temperature': 11.0, 'start_temperature': 15.7, 'desired_temperatures': [26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26]}}, {}, {}], 'outdoor_temperature_forecast': [15, 15, 15, 15, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 18, 18, 21, 21, 23, 23, 25, 25, 27, 27, 27, 27, 28, 28, 28, 28, 28, 28, 27, 27, 25, 25, 23, 23, 21, 21, 21, 21, 20, 20], 'alpha': 1, 'beta': 0, 'num_def_loads': 6, 'def_total_hours': [0, 9, 0, 6, 9, 4], 'def_end_timestep': [0, 0, 0, 0, 0, 0], 'P_deferrable_nom': [300, 6333, 11520, 11000, 600, 11520], 'def_current_state': [0, 0, 0, 0, 0, 0], 'def_start_penalty': [1, 1, 1, 1, 1, 1], 'treat_def_as_semi_cont': [1, 1, 0, 0, 1, 0], 'set_def_constant': [0, 0, 0, 0, 0, 0], 'soc_init': 0.11, 'soc_final': 1} |
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Another thing in my local repo is dumping the whole LP file to the output
directory for debugging purposes.
Unfortunately I don't see a tool for PuLP that can explain why the problem
is infeasible, but I suppose in theory one could throw something together
that tries discarding constraints until the thing works.
I'll have to see if I can pull these out into PRs.
— Andrew Garrett
…On Thu, 3 Oct 2024 at 08:28, Mark Purcell ***@***.***> wrote:
I find I have feasibility issues quite often. I think one common cause is
setting a desired temperature for the first or second timestep - if that
temperature isn't being met right now, then the whole thing is infeasible
because the system has no time to meet it.
It would be great if we could get some more debug logging with the thermal
model so we can see what is going on under the covers.
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The heating constant's natural definition is energy input * CoP / thermal
mass. So for a 100kL swimming pool with a 30 kW nominal (inclusive of CoP)
heat pump, you would start at 30 kWh / (100 000 kg * 4.2 J/g/°C). You can
observe it experimentally by running the load at full blast for an hour at
a time when the impact of the outdoor temperature is minimised (ie outdoor
temperature close to indoor temp) and recording the change in temperature.
The coupling to outdoor temperature (ie how well insulated it is) would be
captured by the "cooling constant". That one you really do need to figure
out experimentally, though it's easier to do because you can observe it in
isolation by turning off the load for an hour, ideally at a time with a big
difference between outdoor and indoor temperature, and dividing the change
in temperature by the difference between outdoor and indoor temperature
(and take the absolute value of the result.
For debugging these values, I've set up the model to export an extra
variable with the forecasted temperature. I'm displaying it in Plotly with
this snippet (on y axis 6 because I have lots of charts)
- entity: sensor.emhass_temp_predicted2
attribute: predicted_temperatures
yaxis: y6
filters:
- fn: |-
({xs, ys, meta, states, statistics, hass, vars}) => {
const forecasts =
states[states.length-1].attributes.predicted_temperatures;
res = {
xs: forecasts.map(fc => fc["date"]),
ys: forecasts.map(fc => fc["emhass_temp_predicted2"]),
meta: meta
};
return res;
}
— Andrew Garrett
…On Tue, 8 Oct 2024 at 01:47, martinarva ***@***.***> wrote:
So do i understand correctly that I should have my own logic for the
heating constant that will take account of the CoP outside temp correlation
etc?
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As my heating is not turned on yet, i could do the cooling constant data collection and initial calculations of the cooling constant. This part is actually pretty straight forward and easy to do. More tricky is figuring out the heating rate. I have under floor water heating and if i run the heating on full blast for one hour, there is a quite big inertia and the room tempo will start rising maybe 2-3 hours later and then continue heating for several hours. As far is I understand there is no delay variable at the moment. |
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Hi! Anybody already an example that works with an heat pump? Kind regards, Rick |
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i am running a preceeding EMHASS with 30 minute timelags and 3 deferrable loads. All 3 having their own timewindow, set by the "def_start_timestep"and "def_end_timestep". All this was working very well before I added the thermal model. My house is heated with a all-electric Daikin heatpump and using radiators only, no floor heating and no cooling.
So far I have the following remarks:
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What weather integration do you use to get a forecast ? |
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I am using the ha-solcast-solar integration from |
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I meant by sensor.emhass half hourly outside temperature sensor? |
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I am using the Met.no integration which is quite a standard in Home Assistant, but I think any other weather service providing hourly temperatures will do. This will create a sensor, in my case 'sensor.forecast_home_hourly', with a lot of attributes. The last element in the list, 13, is just a fake temperature to make sure the list is always containing 48 elements. |
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I've finally found a few hours to do some things I've been thinking about for a while here, and I came up with #374 See details there, but basically my hope is that these changes will fix a lot of the feasibility issues folks have been encountering. |
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Great! Thanks for your contribution Andrew. |
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I have started this thread to document my setup and use of the thermal model within EMHASS.
I hope others can share their experiences.
Currently I have an adhoc thermal model which I want to transition to the EMHASS model.
Warm winter days here so I am playing with my cooling, which is recorded in my Load Power Forecast.
I have been playing around with charging p_nom_power and def_total_run_hours for my HVAC..
I hope the new thermal model will address my setup..
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