Data acquisition

In a big data context the acquisition of data is seen in relation to the phenomenon of datafication as described in 2.2.5. This applies to acquisition of smart meter data as well, where the aggregation of millions of individual meter readings, when aggregated and put in a system can yield the foundation for new knowledge, intelligent systems and new revenue streams.

Smart meters provide four different types of data: power consumption data, generation data, event data, and power quality data. Power consumption data is the most relevant data type for this thesis. However, it must be noted that the other data types has their relevance for most applications, but the focus through this analysis is on power consumption data, which is further divided into the following:

1. Detailed consumption data:15 to 60 min interval readings of electricity consumption;

2. Billing interval data:Readings at the beginning and end of billing intervals;

3. Aggregate statistical data:Monthly consumption, comparisons and history;

4. Broadcast data:Communication to the use about price chance, critical peaks and reliability;

From the above mentioned, the by far most used measurement data is detailed consump-tion data, henceforth referred to as consumpconsump-tion data and is the main type of concern.

Smart meters ability to provide detailed information from consumer energy consumption is unprecedented. The higher the resolution the more information can be inferred. Dif-ferent algorithms[77, 78]can used to identify appliances in the household and predict the behavior of customers. Figure 4.2 illustrate how a higher data resolution yield different richness of information about consumption. At half-hour intervals indicative periods of

Chapter 4. Case study: Smart Meter Data

consumption can be inferred, giving insight to for example occupancy of the household.

At 1 minute intervals on the other hand appliances can be identified due to their load signatures.[11]Taking it to the extreme: by using a 0,5s^-1sampling rate Greveler et al.[79] were able to reveal the type of TV-channel and even using the power profile to identify the content of the media that was displayed on TV-screens.

Figure 4.2:Examples of information inferred from different data resolutions[80]

Smart meters are undoubtedly able to provide valuable information about a the household and the resolution is a decisive factor to the richness of the information. Larger datasets and more granular data yield a higher probability for data mining algorithms to discover hidden patters and for learning algorithms to be accurate with their predictions.[14] However, as more information is inferred the more intrusive smart metering becomes and the potential for revealing personal and sensitive information increases[11, 80]. The global consulting firm refers to smart meters as a "gateway to the home"[10]which pretty much summarizes the controversy of smart metering. Summarized in figure 4.2 are some privacy concerns that arises with a high resolution.

Table 4.2:Examples of interested parties and their intentions (Adopted from[81])

Interested parties Purpose

Insurance companies Determine health premiums based on behavior indicating illness Marketers Target advertising

Creditors Determining behavior that might indicate creditworthiness Law enforcements Identification of suspicious or illegal activity

Criminals Identification of best times for burglary and valuable appliances Civil litigators Identification of property boundaries and activities on the premise Landlords Verification of lease compliance

Private investigators Monitoring of specific events

Press Get information about famous people

4.3. Applying big data analytics to smart meter data

The GDPR addresses this issue and will directly curtail the leeway to take advantage of higher resolutions. Particularly relevant provisions are the dataminimisation principle. It will directly prohibit excessive collection by resolutions higher than what is necessary to achieve the purposes informed about in the customer consent

Additionally, because ofprivacy by default customers with smart meters will by default provide the least sensitive information necessary for the purpose of the processing. This will most likely mean that hourly to half-hourly sampling rates will be default and an "opt-in" consent will be needed for any higher resolutions. As stated previously it is assumed to be 60 min by default in the following.

This is a classic case of "chicken or the egg". The optimal situation for the utility is to collect as much consumption data as possible, namely at the highest possible resolution, but is restricted by design in the GDPR. The only way to obtain this information will be through a fresh consent, which then will become the only way to maximize the value from the smart meter.

However, if data protection authorities find it necessary to obtain higher resolutions for purposes in "public interest" this may open for additional processing opportunities for utilities. For example letting utilities sample at necessary resolutions for processing in public interest, but only process at agreed upon resolutions for purposes provided in the customer consent. However, the understanding of the author is that the GDPR does not open for such actions.

Processing smart meter data

Utilities processes data in real-time to provide decision-support in demand response and operation of the the grid. The ability to process data in real-time is of great importance to ensure reliability in the grid. Real-time processing provide the ability to monitor the status of the grid, and modern technology know how to utilize this data to ensure efficiency and resilience.[82]Figure 4.3 illustrate the importance of timely data in decision-making where smart meters provide the infrastructure to monitor the grid all the way to the end-user.

Enabling proactive pricing and execution of load control during emergencies.[75]

Chapter 4. Case study: Smart Meter Data

Figure 4.3:Value from speed of processing[32]

In addition to secure grid operations, real-time processing provide customers timely feed-back giving them increased flexibility to respond to demand in the market. The value of processing speed is manifested in figure 4.4 where the real-time feedback yields the largest energy savings.

Figure 4.4:Value of timely feedback (adopted from[83])

4.3. Applying big data analytics to smart meter data

A higher resolution enable more detailed feedback, for instance about appliances that consume the most power, and at what time of day they should and should not be used.

Hence, it comes to show that customers as well as society as a whole can benefit largely from high resolution data. This is, however a two-egged-sword: on one side, high resolution means high risk to privacy, while on the other side, providing detailed information will save customers money and ultimately also benefit the environment. However, consumers can not be forced to opt-in for more detailed feedback. A voluntary explicit consent must therefore be obtained.

Because of the seemingly high savings potential from feedback programs these could even-tually become enablers for utilities collect at higher resolutions. This assuming that utilities won’t be able to collect at higher resolution without customer consent for public interest purposes. Such a feedback program would arguably be opted for through a user friendly smartphone app.

Chapter 4. Case study: Smart Meter Data