Our interview with HANNO SCHOKLITSCH, CEO at Kaiserwetter Energy Asset Management GmbH once again charts the inner-workings of a firm changing the world of wind and solar asset management. Executive Global explore the impact of smart data analytics, machine learning and predictive analytics upon the portfolios of renewable investors as we delve deeper into the realm of sustainable investing.

EG: How may High Net Worth and institutional investors benefit from investing in renewable energy in 2019?


HS: Institutional investors can and should take advantage of the significant pull of renewable energy this year. 2019 is an important year in the sector in order to comply with the agreements that had been agreed for 2020, so renewables need more investment to comply with them.

In addition, the innovation that is being developed around renewable energy, such as Kaiserwetter’s IoT platform, ARISTOTELES, allows investors to maximise their profits, minimise investment risks and to create the highest transparency standards in order to attract more capital.

The IoT platform uses the possibilities offered by digitisation and intelligent data analysis. Based on smart data analytics, predictive analytics, and machine learning, we can achieve a decisive performance improvement of renewable energy assets and entire portfolios. ARISTOTELES aggregates technical, meteorological, and in particular- financial data, transforming unstructured data into a structured database.

EG: Germany is working towards having renewable energy sources supply half its electricity by 2030. Is now the perfect time to invest in renewable energy?

HS: Germany has set a target of generating 65% of its power from renewable sources by 2030, requiring an increase in wind and solar generation capacity to between 215 and 237 gigawatts (GW) from the current level of 120 GW now. Renewables account for about 44% now.

Investments in renewable energy were halved in Germany in 2018 compared to the year before, landing the country in fourth place in global renewable investments. Germany leads in Europe if the investments over the past ten years are taken into account, but a drop in wind power investments brought total financial flows in renewable technology down significantly. However, since prices for renewable power installations, especially for solar power, have also fallen sharply over the past decade, Germany’s capacity growth has not fallen as much as monetary investment and has also remained positive during last year.

EG: In what ways may digitisation and intelligent data analysis improve productivity in this sector?

HS: Kaiserwetter is a pioneer and leader in the digitalisation of renewable energy worldwide. It provides Data Analytics as a Service (DAaaS) to persuade investors and financing institutions in the renewable sector to invest more in renewable energy and thus accelerate the flow of investments towards emission-free energy production.

The company is dedicated to IoT, Smart Data Analytics, Predictive Analytics and Artificial Intelligence, with a focus on renewable energy and because of that, it is showing the world how data intelligence and digital innovations can contribute to help stop climate change. Thanks to the integration of technical, financial and meteorological data, we are able to maximise returns and minimise investment risk, which increases the performance of the asset portfolio. 

EG: How would you define the perfect portfolio of renewable energy investments?

That depends as always, on the risk-return-ratio an investor is going to take and what is defined within their investment strategy. In the end, the investor has to decide what risks he is ready to take to achieve a certain predefined IRR and this automatically leads to a geographical investment allocation. The technology to be chosen depends on the local conditions and the regulations implemented by authorities for different asset classes. We can contribute to the investment decision process as our clients can take advantage of our benchmarking capabilities showing the best technology to be installed in certain areas, while knowing the OPEX to be planned.

EG: Tell us about how your technologies may combine with cryptocurrency to aid investors in the future?

The situation could not be more divergent for cryptocurrency and renewable energy. The former face blockades from large companies and banks such as Lloyd’s, which has recently banned the purchase of digital currencies with its credit cards, joining the Americans- JP Morgan Chase or Citi. Renewables, on the other hand, cannot enjoy better health after the Paris Agreement and the Marrakesh Climate Summit, which committed countries and investors to adding to the almost $300 billion already spent annually on the sector, with another $100 billion a year until 2025, a figure that should also increase after that date.

EG: According to Goldman Sachs, double digit growth is expected for utility-scale solar capacity in 2019-2020. What opportunity does this present for investors?​

Solar energy investors may find good investment opportunities in utility-scale solar capacities, but also on smaller solar parks in 2020. It depends on the aforementioned risk-return-ratio and the energy market price, but falling investment costs make the investment in solar parks quite attractive, especially by having a grid-parity situation or better- as the LCOE (Levelized costs of energy) are already below conventionally fired power stations. Therefore, investing in utility-scale solar parks could be an attractive investment, but the entire performance should be monitored by digital technology and algorithms to ensure the planned ROI. 

EG: How can investment managers successfully utilise IoT based technologies for greater returns?

HS: Improving investment risk assessment is now possible, thanks to IoT-driven data analytics platforms. Thanks to ARISTOTELES and our implemented algorithms, we can use AI processes to determine the best possible performance of a wind turbine- hence an entire wind farm. In some rare cases our IoT platform called ARISTOTELES, has detected potential improvements to be made of up to 20%, but even with smaller improvements of 3-4% to be made, the impact on the investment rate of return can be substantial. In other words, harnessing AI and technology will determine the split between winners and losers.

EG: What does the future hold for predictive analytics and machine learning in relation to investment management?

Just like unpredictable weather conditions, the operational yield of renewable energy assets had previously been mostly impossible to foresee. Automated data compilation was difficult to implement and collecting data from different energy portfolios, was too time-consuming. In addition, the analysis of high data volumes oftentimes tested the limits of what investors and operators could do. As a consequence, reliable information about the technical status of assets and ultimately their future yield- was hard to come by.

Kaiserwetter has recently presented a new innovative predictive analytics approach jointly developed with SAP: A new feature is that ARISTOTELES, relies on Machine Learning that uses historical technical data of wind turbines, in order to feed the self-learning algorithms. These algorithms have been programmed to recognise potential technical failures on an early stage, which could lead to a negative impact on the investment. This allows investors to react to reduced yields proactively and at an early stage.   EG