PPC implements EU legislation and policies
PPC, as the biggest energy producer and provider in Greece, has its own strategies for adopting and respecting the abovementioned EU legislation and policies, such as the developed platform “myEnergyCoach” which is aligned with EU policies. The general direction is to promote energy efficient strategies through consumption monitoring and evaluation. It compares household appliances and distinguishes between the eco and non-eco-friendly ones. Additionally, it enables consumers to set an energy-saving goal and provides personalized tips towards this goal. This is an incentive for any household to be on track with EU legislation.
However, through the participation in the EVIDENT project, PPC benefits a series of advantages in this direction. In EVIDENT, new policy strategies are designed by experienced researchers which will provide a competitive advantage to Policy Makers by offering them an easy way to design and evaluate new policies. Advanced features developed within EVIDENT such as big data analytics and visualization can help policy makers to design more creative and better targeted interventions. Under this framework, trial-and-error setting of policy interventions can be avoided and thus, reduce its cost while increasing its effectiveness. Moreover, previous results, data as well as policy measures stored in EVIDENT platform can be used by policy makers as a reference/best practices in order to design new and more effective policies.
Energy consumption data analysis
→ Data and methods
Energy analytics is the process of gathering data, using software, with the purpose of analysis, supervision, and optimization of energy related KPIs (eg. production costs, consumption or production distribution). An efficient energy analytics software harnesses the nuances of the data produced within the various energy sectors and employs it more beneficially. PPC provides an energy dashboard, which can improve profit margins as well as manipulate and understand large-scale trends in the industry. Applying data analytics to the energy sector provides a deeper insight across all the dimensions and in particular, in energy savings. To get specifics regarding the energy usage, it is potentially required only one tool: an integrated platform with electricity usage or energy consumption monitor that provides precise information about the amount of kWh of a device or home appliance. This integrated tool is the ultimate goal of EVIDENT since it is vital in simulating and visualizing consumer behavior.
Since residential electricity consumption has strong temporal variation, which is not captured with low resolution consumption data such as monthly bills, long-term consumption data are required. By making use of this data, the surveys are linked to the smart meters databases through encoded household meter numbers, for confidentiality reasons and for respecting the GDPR. Combining these two sets of information allows an extensive and coherent big data analysis. By evaluating the survey results for the dwellings in each cluster, it is possible to identify important similarities and differences regarding socio economic determinants, dwellings characteristics and appliances use and ownership, that could explain the different clusters’ aggregation and consumption profiles.
→ Classified energy consumption data analysis
The very common classifications include residential, commercial, and industrial/manufacturing consumption data analysis.
Residential data analysis: involves household characteristics, home energy use and costs, and detailed household data processing. Housing characteristics data collected should include fuels used and end-uses in structural and geographic characteristics, such as electronics, lighting, heating and cooling, household unit, demographics, energy insecurity, as it shown in Figure 2. For primary housing units, energy consumption and expenditures can be calculated with end-uses and fuel consumption. In residential energy consumption investigation, energy-related random data are collected.
Commercial data analysis: involves building characteristics, commercial energy use and costs, as well as detailed buildings’ data processing. Detailed tabulated data may be applied and may contain characteristics of commercial buildings, such as buildings size, age, and their usage, energy sources used in the buildings, even geographic location, and other energy-consuming devices and units, as it is shown in Figure 2.
Manufacturing data analysis: involves the characteristics of consumption of energy for all purposes by an industry consisting of systems, buildings, devices, and other energy consumers.
There are many relevant studies by exploring the literature online:
- Suggested methods for analyzing various building energy use cases by user groups . Recent market, technology and policy drivers have resulted in widespread data collection by stakeholders across the buildings industry. Consolidation of independently collected and maintained datasets presents a cost-effective opportunity to build a database of unprecedented size. Applications of the data include peer group analysis to evaluate building performance, and data-driven algorithms that use empirical data to estimate energy savings associated with building retrofits.
- Research suggesting how to predict building energy requirements and energy supply costs by using sensitivity analysis . Detailed consumption data can be used to offer advanced services and provide new business opportunities to all participants in the energy supply chain. The survey proposes an innovative tool that combines the estimation of the building energy consumption and supply costs with a sensitivity analysis.
- Guidelines on measuring energy saving needs by building energy management projects . They include minimum compliance requirements to insure a fair level of confidence in the savings determination. These requirements are set forth in three specific approaches and include compliance paths for each approach. The approaches include: 1) Whole-building metering, 2) Retrofit isolation metering, and 3) Whole-building calibrated simulation.
- Research on building energy data analysis has been conducted on prediction methods with accuracy guaranteed by applying a model that automatically analyses and predicts schedule data, which applies input data when predicting energy consumption .
The majority of the surveys apply data analytics and clustering analysis to energy consumption data towards energy efficiency, which correlates the nature of the surveys with the EVIDENT’s long-term goals.
 Available at: https://www.dei.gr/en/home/myenergy/myenergycoach/
 Available at: https://www.egalite.org/ecodesign-ed-etichettatura-energetica-leuropa-e-gia-in-ritardo/
 Available at: https://www.eumonitor.eu/9353000/1/j9vvik7m1c3gyxp/vlgqhguavfv8?ctx=vg9pi5ooqcz3&tab=1
 Mathew P., Dunn L., Sohn M., Mercado A., Custudio C., Walter T. (2014). Big-data for building energy performance: Lessons from assembling a very large national database of building energy use, 140,85-93.
 Gruber J., Prodanovic M., Alonso R. (2015). Estimation and analysis of building energy demand and supply costs. Energy Procedia, 83, 216–225.
 ASHRAE. (2002). ASHRAE guideline 14-2002 (1st ed.). ASHRAE.
 Ahn Y., Lee Y., Oh E., Kim B. (2020). Building energy demand prediction model using Fourier transform based schedule analysis algorithm. Korean Journal of Air-Conditioning and Refrigeration Engineering, 32, 386–397.