Can you describe your project in a few words?

CNROpt is an ambitious project to develop an innovative tool for power companies and operators of Charging Station Networks for Electric Vehicles. It will help facilitate the efficient rollout of new Charging Station Locations and the expansion of the existing ones by suggesting the optimal sites for growth, considering both business and customer experience requirements.

Powered by AI, we will develop new AI Algorithms based on recent research in the field of AI and train the models with a rich set of time series data.

These data are the results of anonymous charging session raw data of several hundred of thousand sessions from more than 250 locations in Croatia.

Who will help implement the AI solution?

SLOA LTD is a technology SME founded in Cyprus that has established a branch in Kalamata, Greece, in 2022 for AI R&D, branded as Local AI, developing innovative solutions for sustainability utilizing the power of AI for local and global stakeholders, such as Municipalities, Regional Government, Private Energy Stakeholders, etc.

 AI innovation is at the core of its business strategy, and it actively participates in research and innovation projects to jump-start the development of new functionalities and commercial solutions. We envision a future in which AI is ubiquitous and assists local, regional, and global green transition initiatives. We work with the University of Zagreb, with their deep domain knowledge of the EV charging network technologies, to provide a disruptive way to develop Charging Networks in Europe.

What is the AI solution the project plans to implement?

For the Timeseries Forecasting models, we plan to try three algorithms:

1. Prophet off-the-self algorithm, a widely used procedure for forecasting time series data based on an additive model where non-linear trends fit yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series with substantial seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend and typically handles outliers well. The Prophet procedure includes many possibilities for users to tweak and adjust forecasts. By adding your domain knowledge, we will use human-interpretable parameters to improve our projections.
2. Transformer Architecture for Timeseries. Transformers constitute a class of Deep Learning Models suited to handle temporal dependencies on data. They are the backbone of modern Language Models (GPT. We will develop our custom Transformer-based prediction model to perform the congestion-utilization prediction.
3. LSTM. Long short-term memory (LSTM) is an artificial neural network in artificial intelligence and deep learning. Unlike standard feedforward neural networks, LSTM has feedback connections. Such a recurrent neural network (RNN) can process not only single datapoints (such as images) but also entire sequences of data (such as speech or video). This characteristic makes LSTM networks ideal for processing and predicting data.

For the Candidate Stations Scoring – Selection modelling, we will approach this problem by trying to geographically cluster the existing Charging Stations with the K-means Clustering in Geographical Dimensions based on various KPIs (RoI, Utilization, Congestion, waiting times, etc.) as the previous step produces them, the Timeseries Forecasting. This will help us detect the geographical areas indicating hot spots for the Charging Station Network, prioritizing the relevant candidate sites for expansion.