GB demand flexibility service savingSessions

Analysis of GB electricity demand and twitter data

Author

Ben Anderson (@dataknut)

Published

March 24, 2023

1 Introduction

UK ‘demand flexibility service’ DFS experiments/trials/‘live’ events by NG-ESO and retailers such as @OctopusEnergy

DFS began on 1 November 2022.
The service will run until March 2023.

Relevant media articles:

Monday 23rd Jan 2023: https://www.bbc.co.uk/news/business-64367504
Tuesday 24th Jan 2023: https://www.bbc.co.uk/news/business-64372264

Questions:

Q1: Is there a noticeable ‘dip’ in demand? (Can we even tell?)

Q2: Do tweets tell us anything useful about the events?

2 Code setup

Part of https://github.com/dataknut/savingSessions

[1] "Loading libraries..."

data.table      dplyr  flextable    ggplot2   hashTagR        hms      knitr 
      TRUE       TRUE       TRUE       TRUE       TRUE       TRUE       TRUE 
kableExtra  lubridate      readr     rtweet   tidytext  wordcloud 
      TRUE       TRUE       TRUE       TRUE       TRUE       TRUE

3 Data

3.1 Electricity demand and carbon intensity data

Use the half-hourly NG-ESO ‘generation mix’ data as a reasonable proxy for ‘demand’. This also includes a useful estimate of carbon intensity.

Should probably use the demand data instead as it includes embedded wind & solar.

Variables of interest:

GENERATION is in MW. It has some exclusions, its just an indicator.
CARBON_INTENSITY is in g CO2/kWh - we’d expect this to be high during DFS events (as we’re trying to avoid the use of high carbon & expensive generation?)

3.2 Twitter data

Use rtweet to collect tweets matching:

#savingSession or #savingSessions (not case sensitive) - this seems to be the preferred Octopus hashtag
#DemandFlexibilityService (not case sensitive) - as used by NG-ESO, appears less frequently used

We keep these datasets distinct as it was not clear if all Octopus DFS events were also wider NG-ESO ‘live’ events.

We do NOT store the tweets in the repo for both ethical and practical reasons…

Note also that we may not be collecting the complete dataset of hashtagged tweets due to:

the intricacies of the twitter API.
changes to the twitter API policy which may have reduced the availability of tweets from February 2023. It is currently unclear whether a free API access tier will be available or per-project academic research access will be required.

It is important to note that both datasets are therefore likely to be partial.

3.2.1 #savingSessions tweets

Figure 1 shows the cumulative number of #savingSession tweets collected and marks the relevant events. Labels are likely to overlap where events are close together.

Figure 1: Cumulative number of #savingSession tweets collected

Number of tweets: 6391

Positive words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`


Tweets from Mon 07 Nov 2022 00:00 to Fri 24 Mar 2023 00:00

Negative words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`

Total word and unique word counts…

Number of unique words by sentiment
Var1	Freq
negative	366
positive	312

Number of words by sentiment
sentiment	freq
negative	1603
positive	3943

3.2.2 #demandFlexibilityService tweets

Figure 2 shows the cumulative number of #DemandFlexibilityService tweets collected and marks the relevant events. Labels are likely to overlap where events are close together.

NB: data collection for this hashtag only started on 23rd January - it looks like twitter’s search API does not return tweets more than x days old. People were tweeting this hashtag earlier than January…

[1] "Found 85 files matching *.csv in /Users/ben/Dropbox/data/twitter/demandFlexibilityService/"

Number of tweets: 328

Figure 2: Cumulative number of #DemandFlexibilityService tweets collected

Positive words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`


Tweets from Sun 22 Jan 2023 00:00 to Wed 15 Mar 2023 00:00

Negative words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`

Total word and unique word counts…

Number of unique words by sentiment
Var1	Freq
negative	29
positive	40

Number of words by sentiment
sentiment	freq
negative	92
positive	142

4 Analysis

Q1: Demand reduction

Simple comparison of the ‘demand response session’ day half-hourly demand with the mean half-hourly demand for the previous n weekdays or weekend days as appropriate. Default n is 10.

All sorts of caveats apply:

not all of these sessions were national ‘live’ events, some were trials by a single retailer (Octopus)
even though we compare with weekdays or weekend days as appropriate, we may still not be comparing like with like (cold vs warm, holiday vs non-holiday etc)
we can’t tell from this analysis what demand would have looked like without the demand response intervention. That would require all the retailers to aggregate the ‘savings’ they estimate… or a randomised control trial

Q2: Twitter sentiment analysis

Inspired by https://www.tidytextmining.com/sentiment.html (sentiment analysis) and https://towardsdatascience.com/create-a-word-cloud-with-r-bde3e7422e8a (word clouds)

Extract the tweets for the day of each DFS event and the day after.

Remove stop words (to, the, and, a, for, etc) & profanity etc from the tweets and sentiment them.

In each case we report the number of negative and positive codings (according to tidytext::get_sentiments("bing")) for the unique words. This will add up to the Number of unique words by sentiment. We then report the total frequency of words that are negative or positive (which will add up to the total number of words).

Remember the size of the (positive/negative) words in the word cloud is relative to the count of all (positive/negative) words in the wordcloud, not the total word count.

Saving sessions
ID	type	start_hh	end_hh
1	octopus	2022-11-15 17:00:00	2022-11-15 17:30:00
2	octopus	2022-11-22 17:30:00	2022-11-22 18:00:00
3	octopus	2022-11-30 17:30:00	2022-11-30 18:00:00
4	octopus	2022-12-01 17:30:00	2022-12-01 18:00:00
5	octopus	2022-12-12 17:00:00	2022-12-12 17:30:00
6	octopus	2023-01-19 09:00:00	2023-01-19 09:30:00
7	NG-ESO live	2023-01-23 17:00:00	2023-01-23 17:30:00
8	NG-ESO live	2023-01-24 16:30:00	2023-01-24 17:30:00
9	octopus	2023-01-30 09:00:00	2023-01-30 09:30:00
10	octopus	2023-02-13 17:30:00	2023-02-13 18:00:00
11	octopus	2023-02-21 17:30:00	2023-02-21 18:00:00
12	NG-ESO live	2023-03-15 18:30:00	2023-03-15 19:00:00
13	octopus	2023-03-23 18:30:00	2023-03-23 19:00:00

4.1 Session 1 (Octopus: Tue 15 Nov 2022 17:00)

2022-11-15 17:00:00 to
2022-11-15 18:00:00

(Not all retailers may have taken part in this one)

According to https://energycentral.com/news/octopus-energy-provide-108mw-grid-flexibility-first-%E2%80%98saving-session%E2%80%99:

“Over 200,000 households reduced their energy demand by 108MW collectively, the same as a gas power station can generate in an hour. If replicated by all UK energy suppliers, this would be over 1GW.”

But https://www.current-news.co.uk/news/octopus-energy-customers-provide-108mw-of-flexibility-during-first-saving-session quotes Octopus as actually saying:

“if the program was scaled to all electric smart meter customers with all supplier[s] in Great Britain, it could create over 1GW of flexible energy load”

The following plot compares GB electricity demand during the DFS event with mean GB electricity demand over the last n similar days (i.e. weekdays or weekends as appropriate).

MW comparisons
hms	Comparison mean	Saving session	diff	pc_diff
17:00:00	41,153.5	42,925.0	1,771.5	4.3
17:30:00	41,173.7	42,736.0	1,562.3	3.8

Confounding factors:

recent temperature trends
recent holidays
etc

Note that the comparison plot/table shows the difference between the DFS event day and the previous similar days. This does not show what the event day energy use would have been in the absence of the intervention - we’d need a randomised control on the day for that (or a sophisticated demand model as the counterfactual).

Octopus customers’ 108 MW was 0.252 % of mean GB generation (42.925 GW) over the hour of the session (see NG-ESO data above).

108 MW over 200,000 customers is 5.4^{-4} MW per household or, more sensibly, 0.54 kW per household. A pretty reasonable reduction.

But if 200,000 produced a 108 MW power reduction (0.54 kW per household ), the 1 GW estimate implies we only have 1,851,852 GB households with a smart meter. Really? Or did they mean all suppliers taking part in the scheme?

Anyway, the latest data has 14 million domestic eletricity smart meters as of Q1 2022 - 45% of all domestic meters.

So:

If all ~28 million households in the UK did this it would be 1.512^{7} kW. That’s 15.12 GW…
If we reduce ambition a bit and assume that only the 14 million domestic GB customers with a smart meter as of Q2 2022 did this then that would provide 7.56 GW. Which would be 17.61 % of generation. Not bad aye?

The trouble is… these are octopus customers self-selecting into a trial. You just have to read the tweets to see that there are households with installed batteries, pausable heat pumps in well-insulated homes and EV V2G. At what point will we all be similarly equipped?

4.1.1 Carbon intensity

And just to keep in mind the carbon context:

Figure 3: Comparison of carbon intensity

Turning to hashtags, we only have #savingSessions tweets for this event.

4.1.2 #savingSessions tweets

Number of tweets: 642

Positive words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`


Tweets from Tue 15 Nov 2022 00:00 to Wed 16 Nov 2022 00:00

Negative words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`

Total word and unique word counts…

Number of unique words by sentiment
Var1	Freq
negative	87
positive	104

Number of words by sentiment
sentiment	freq
negative	203
positive	349

4.2 Session 2 (Octopus: Tue 22 Nov 2022 17:30)

2022-11-22 17:30:00 to
2022-11-22 18:30:00

(Not all retailers may have taken part in this one)

The same caveats about the comparisons apply here…

MW comparisons
hms	Comparison mean	Saving session	diff	pc_diff
17:30:00	42,352.4	41,354.0	-998.4	-2.4
18:00:00	42,027.6	40,784.0	-1,243.6	-3.0

4.2.1 Carbon intensity

And just to keep in mind the carbon context:

Figure 4: Comparison of carbon intensity

Turning to hashtags, we only have #savingSessions tweets for this event.

4.2.2 #savingSessions tweets

Number of tweets: 611

Positive words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`


Tweets from Tue 22 Nov 2022 00:00 to Tue 22 Nov 2022 00:00

Negative words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`

Total word and unique word counts…

Number of unique words by sentiment
Var1	Freq
negative	96
positive	92

Number of words by sentiment
sentiment	freq
negative	170
positive	309

4.3 Session 3 (Octopus: Wed 30 Nov 2022 17:30)

2022-11-30 17:30:00 to
2022-11-30 18:30:00

(Not all retailers may have taken part in this one)

The same caveats about the comparisons apply here…

MW comparisons
hms	Comparison mean	Saving session	diff	pc_diff
17:30:00	42,851.4	41,489.0	-1,362.4	-3.2
18:00:00	42,469.4	41,011.0	-1,458.4	-3.4

4.3.1 Carbon intensity

And just to keep in mind the carbon context:

Figure 5: Comparison of carbon intensity

Turning to hashtags, we only have #savingSessions tweets for this event.

4.3.2 #savingSessions tweets

Number of tweets: 728

Positive words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`


Tweets from Wed 30 Nov 2022 00:00 to Thu 01 Dec 2022 00:00

Negative words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`

Total word and unique word counts…

Number of unique words by sentiment
Var1	Freq
negative	77
positive	103

Number of words by sentiment
sentiment	freq
negative	206
positive	490

4.4 Session 4 (Octopus: Thu 01 Dec 2022 17:30)

2022-12-01 17:30:00 to
2022-12-01 18:30:00

(Not all retailers may have taken part in this one)

The same caveats about the comparisons apply here…

MW comparisons
hms	Comparison mean	Saving session	diff	pc_diff
17:30:00	42,810.5	42,194.0	-616.5	-1.4
18:00:00	42,393.3	41,516.0	-877.3	-2.1

4.4.1 Carbon intensity

And just to keep in mind the carbon context:

Figure 6: Comparison of carbon intensity

Turning to hashtags, we only have #savingSessions tweets for this event.

4.4.2 #savingSessions tweets

Number of tweets: 305

Positive words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`


Tweets from Thu 01 Dec 2022 00:00 to Fri 02 Dec 2022 00:00

Negative words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`

Total word and unique word counts…

Number of unique words by sentiment
Var1	Freq
negative	40
positive	62

Number of words by sentiment
sentiment	freq
negative	83
positive	189

4.5 Session 5 (Octopus: Mon 12 Dec 2022 17:00)

2022-12-12 17:00:00 to
2022-12-12 18:00:00

(Not all retailers may have taken part in this one)

The same caveats about the comparisons apply here…

MW comparisons
hms	Comparison mean	Saving session	diff	pc_diff
17:00:00	43,741.7	47,821.0	4,079.3	9.3
17:30:00	43,777.1	48,470.0	4,692.9	10.7

4.5.1 Carbon intensity

And just to keep in mind the carbon context:

Figure 7: Comparison of carbon intensity

Turning to hashtags, we only have #savingSessions tweets for this event.

4.5.2 #savingSessions tweets

Number of tweets: 585

Positive words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`


Tweets from Mon 12 Dec 2022 00:00 to Tue 13 Dec 2022 00:00

Negative words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`

Total word and unique word counts…

Number of unique words by sentiment
Var1	Freq
negative	41
positive	73

Number of words by sentiment
sentiment	freq
negative	138
positive	362

4.6 Session 6 (Octopus: Thu 19 Jan 2023 09:00)

2023-01-19 09:00:00 to
2023-01-19 10:00:00

(Not all retailers may have taken part in this one)

The same caveats about the comparisons apply here…

MW comparisons
hms	Comparison mean	Saving session	diff	pc_diff
09:00:00	40,592.8	45,823.0	5,230.2	12.9
09:30:00	41,228.5	46,740.0	5,511.5	13.4

4.6.1 Carbon intensity

And just to keep in mind the carbon context:

Figure 8: Comparison of carbon intensity

Turning to hashtags, we only have #savingSessions tweets for this event.

4.6.2 #savingSessions tweets

Number of tweets: 248

Positive words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`


Tweets from Thu 19 Jan 2023 00:00 to Fri 20 Jan 2023 00:00

Negative words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`

Total word and unique word counts…

Number of unique words by sentiment
Var1	Freq
negative	22
positive	40

Number of words by sentiment
sentiment	freq
negative	37
positive	85

4.7 Session 7 (NG-ESO ‘live’: Mon 23 Jan 2023 17:00)

2023-01-23 17:00:00 to
2023-01-23 18:00:00

This was a more generalised call for DFS

The same caveats about the comparisons apply here…

MW comparisons
hms	Comparison mean	Saving session	diff	pc_diff
17:00:00	44,751.7	46,086.0	1,334.3	3.0
17:30:00	44,894.9	45,684.0	789.1	1.8

4.7.1 Carbon intensity

And just to keep in mind the carbon context:

Figure 9: Comparison of carbon intensity

Turning to hashtags, we have both #savingSessions and #demandFlexibilityService tweets for this event.

4.7.2 #savingSessions tweets

Number of tweets: 818

Positive words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`


Tweets from Mon 23 Jan 2023 00:00 to Tue 24 Jan 2023 00:00

Negative words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`

Total word and unique word counts…

Number of unique words by sentiment
Var1	Freq
negative	92
positive	94

Number of words by sentiment
sentiment	freq
negative	245
positive	417

4.7.3 #demandFlexibilityService tweets

Number of tweets: 169

Positive words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`


Tweets from Mon 23 Jan 2023 00:00 to Tue 24 Jan 2023 00:00

Negative words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`

Total word and unique word counts…

Number of unique words by sentiment
Var1	Freq
negative	23
positive	32

Number of words by sentiment
sentiment	freq
negative	69
positive	113

4.8 Session 8 (NG-ESO ‘live’: Tue 24 Jan 2023 16:30)

2023-01-24 16:30:00 to
2023-01-24 18:00:00

This was another generalised call for DFS:

The same caveats about the comparisons apply here…

Also this event followed very soon after the last one so this will affect the comparison ‘baseline’ (previous n days).

MW comparisons
hms	Comparison mean	Saving session	diff	pc_diff
16:30:00	44,093.7	44,975.0	881.3	2.0
17:00:00	44,965.3	45,275.0	309.7	0.7
17:30:00	45,027.4	44,814.0	-213.4	-0.5

4.8.1 Carbon intensity

And just to keep in mind the carbon context:

Figure 10: Comparison of carbon intensity

Turning to hashtags, we have both #savingSessions and #demandFlexibilityService tweets for this event.

4.8.2 #savingSessions tweets

Number of tweets: 687

Positive words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`


Tweets from Tue 24 Jan 2023 00:00 to Wed 25 Jan 2023 00:00

Negative words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`

Total word and unique word counts…

Number of unique words by sentiment
Var1	Freq
negative	82
positive	76

Number of words by sentiment
sentiment	freq
negative	224
positive	324

4.8.3 #demandFlexibilityService tweets

Number of tweets: 72

Positive words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`


Tweets from Tue 24 Jan 2023 00:00 to Wed 25 Jan 2023 00:00

Negative words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`

Total word and unique word counts…

Number of unique words by sentiment
Var1	Freq
negative	9
positive	19

Number of words by sentiment
sentiment	freq
negative	37
positive	61

4.9 Session 9 (Octopus: Mon 30 Jan 2023 09:00)

2023-01-30 09:00:00 to
2023-01-30 10:00:00

The same caveats about the comparisons apply here…

MW comparisons
hms	Comparison mean	Saving session	diff	pc_diff
09:00:00	43,861.3	42,191.0	-1,670.3	-3.8
09:30:00	44,589.2	42,699.0	-1,890.2	-4.2

4.9.1 Carbon intensity

And just to keep in mind the carbon context:

Figure 11: Comparison of carbon intensity

Turning to hashtags, we have both #savingSessions and #demandFlexibilityService tweets for this event.

4.9.2 #savingSessions tweets

Number of tweets: 135

Positive words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`


Tweets from Mon 30 Jan 2023 00:00 to Tue 31 Jan 2023 00:00

Negative words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`

Total word and unique word counts…

Number of unique words by sentiment
Var1	Freq
negative	21
positive	42

Number of words by sentiment
sentiment	freq
negative	31
positive	85

4.9.3 #demandFlexibilityService tweets

Unlikely to be many of these as this was not a system-wide NG-ESO ‘live’ event.

Number of tweets: 12

Positive words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`


Tweets from Mon 30 Jan 2023 00:00 to Tue 31 Jan 2023 00:00

Negative words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`

Total word and unique word counts…

Number of unique words by sentiment
Var1	Freq
negative	1
positive	3

Number of words by sentiment
sentiment	freq
negative	2
positive	3

4.10 Session 10 (Octopus: Mon 13 Feb 2023 17:30)

2023-02-13 17:30:00 to
2023-02-13 18:30:00

The same caveats about the comparisons apply here…

MW comparisons
hms	Comparison mean	Saving session	diff	pc_diff
17:30:00	42,913.7	40,609.0	-2,304.7	-5.4
18:00:00	42,434.7	40,555.0	-1,879.7	-4.4

MW comparisons

4.10.1 Carbon intensity

And just to keep in mind the carbon context:

Figure 12: Comparison of carbon intensity

Turning to hashtags, we have both #savingSessions and #demandFlexibilityService tweets for this event.

4.10.2 #savingSessions tweets

Number of tweets: 191

Positive words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`


Tweets from Mon 13 Feb 2023 00:00 to Tue 14 Feb 2023 00:00

Negative words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`

Total word and unique word counts…

Number of unique words by sentiment
Var1	Freq
negative	19
positive	42

Number of words by sentiment
sentiment	freq
negative	38
positive	174

4.10.3 #demandFlexibilityService tweets

Unlikely to be many of these as this was not a system-wide NG-ESO ‘live’ event.

Number of tweets: 15

Positive words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`


Tweets from Mon 13 Feb 2023 00:00 to Tue 14 Feb 2023 00:00

Negative words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`

Total word and unique word counts…

Number of unique words by sentiment
Var1	Freq
negative	1
positive	2

Number of words by sentiment
sentiment	freq
negative	2
positive	6

4.11 Session 11 (Octopus: Tue 21 Feb 2023 17:30)

2023-02-21 17:30:00 to
2023-02-21 18:30:00

The same caveats about the comparisons apply here…

MW comparisons
hms	Comparison mean	Saving session	diff	pc_diff
17:30:00	41,665.0	39,528.0	-2,137.0	-5.1
18:00:00	41,248.7	39,276.0	-1,972.7	-4.8

MW comparisons

4.11.1 Carbon intensity

And just to keep in mind the carbon context:

Figure 13: Comparison of carbon intensity

Turning to hashtags, we have both #savingSessions and #demandFlexibilityService tweets for this event.

4.11.2 #savingSessions tweets

Number of tweets: 95

Positive words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`


Tweets from Tue 21 Feb 2023 00:00 to Wed 22 Feb 2023 00:00

Negative words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`

Total word and unique word counts…

Number of unique words by sentiment
Var1	Freq
negative	4
positive	14

Number of words by sentiment
sentiment	freq
negative	12
positive	35

4.11.3 #demandFlexibilityService tweets

Unlikely to be many of these as this was not a system-wide NG-ESO ‘live’ event.

Number of tweets: 4

Positive words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`

Warning in max(freq): no non-missing arguments to max; returning -Inf

Warning in max(freq): no non-missing arguments to max; returning -Inf

Error in strwidth(words[i], cex = size[i], ...) : invalid 'cex' value


Tweets from Tue 21 Feb 2023 00:00 to Wed 22 Feb 2023 00:00

Negative words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`

Warning in max(freq): no non-missing arguments to max; returning -Inf

Warning in max(freq): no non-missing arguments to max; returning -Inf

Error in strwidth(words[i], cex = size[i], ...) : invalid 'cex' value

Total word and unique word counts…

Number of unique words by sentiment
Var1	Freq
negative	4
positive	14

Number of words by sentiment
sentiment	freq
negative	12
positive	35

4.12 Session 12 (NG-ESO ‘live’ (?): Wed 15 Mar 2023 18:30)

2023-03-15 18:30:00 to
2023-03-15 19:30:00

The same caveats about the comparisons apply here…

MW comparisons
hms	Comparison mean	Saving session	diff	pc_diff
18:30:00	42,237.6	41,357.0	-880.6	-2.1
19:00:00	40,892.1	40,730.0	-162.1	-0.4

MW comparisons

4.12.1 Carbon intensity

And just to keep in mind the carbon context:

Turning to hashtags, we have both #savingSessions and #demandFlexibilityService tweets for this event.

4.12.2 #savingSessions tweets

Number of tweets: 90

Positive words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`


Tweets from Wed 15 Mar 2023 00:00 to Thu 16 Mar 2023 00:00

Negative words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`

Total word and unique word counts…

Number of unique words by sentiment
Var1	Freq
negative	13
positive	29

Number of words by sentiment
sentiment	freq
negative	27
positive	63

4.12.3 #demandFlexibilityService tweets

Number of tweets: 1

Positive words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`


Tweets from Wed 15 Mar 2023 00:00 to Wed 15 Mar 2023 00:00

Negative words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`

Warning in max(freq): no non-missing arguments to max; returning -Inf

Warning in max(freq): no non-missing arguments to max; returning -Inf

Error in strwidth(words[i], cex = size[i], ...) : invalid 'cex' value

Total word and unique word counts…

Number of unique words by sentiment
Var1	Freq
positive	1

Number of words by sentiment
sentiment	freq
positive	1

4.13 Session 13 (Octopus (?): Thu 23 Mar 2023 18:30)

2023-03-23 18:30:00 to
2023-03-23 19:30:00

The same caveats about the comparisons apply here…

MW comparisons
hms	Comparison mean	Saving session	diff	pc_diff
18:30:00	39,905.6	38,362.0	-1,543.6	-3.9
19:00:00	38,711.5	37,611.0	-1,100.5	-2.8

MW comparisons

4.13.1 Carbon intensity

And just to keep in mind the carbon context:

Turning to hashtags, we have both #savingSessions and #demandFlexibilityService tweets for this event.

4.13.2 #savingSessions tweets

Number of tweets: 108

Positive words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`


Tweets from Thu 23 Mar 2023 00:00 to Fri 24 Mar 2023 00:00

Negative words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`

Total word and unique word counts…

Number of unique words by sentiment
Var1	Freq
negative	19
positive	50

Number of words by sentiment
sentiment	freq
negative	22
positive	75

4.13.3 #demandFlexibilityService tweets

Number of tweets: 0

Positive words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`

Warning in max(freq): no non-missing arguments to max; returning -Inf

Warning in max(freq): no non-missing arguments to max; returning -Inf

Error in strwidth(words[i], cex = size[i], ...) : invalid 'cex' value


Tweets from Thu 23 Mar 2023 00:00 to Fri 24 Mar 2023 00:00

Negative words…

Joining with `by = join_by(word)`
Joining with `by = join_by(word)`

Warning in max(freq): no non-missing arguments to max; returning -Inf

Warning in max(freq): no non-missing arguments to max; returning -Inf

Error in strwidth(words[i], cex = size[i], ...) : invalid 'cex' value

Total word and unique word counts…

Number of unique words by sentiment
Var1	Freq
negative	19
positive	50

Number of words by sentiment
sentiment	freq
negative	22
positive	75

5 Future work

more trial days
use 5 days before/after as comparators? Confounded by events close together…