Managing a physical real estate portfolio requires a lot of experience and planning. Early indicators can help optimize refurbishment and maintenance planning even without thousands of IoT sensors. Can an algorithm optimize which buildings and apartments should receive significant investment to keep asset value high and tenants happy?
Some municipal utility providers would like to optimize their trading activities. Here, we set our focus to one special contract of the natural gas market, the so-called day ahead. Intraday and short-term price prediction of this specific product can help municipal utility providers to optimize their trading activities with regard to storage operation, balancing group management and portfolio management. Can we design algorithms to forecast intraday and short-term price movements in this context?
To help keep the Ruhr area safe, RAG conducts flights that measure exact ground elevation levels. These measurements reveal ground depressions that can be early indicators of structural collapse and cause property damage in the future. Spotting these depressions is a difficult task. Can we develop an algorithm that spots ground depressions better than the human eye?
Keeping reliable tenants happy is of high importance to landlords. There are many early indicators before a tenant cancels their contract. Their decision might be explained by increasing rent or by complaining about the same issue several times. Can we develop an early warning system for tenants that are about to leave?
Critical infrastructure requires the highest level of data quality. But outdoor sensors are exposed to rough environments and not always deliver the most accurate information. Dirt, aging of sensors and vandalism are typical reasons for data errors. Manual data correction requires a lot of experience, focus and time. It does not scale well with the increasing number of sensors installed. Can smart algorithms come to the rescue?
Yearly electricity demand is as varied as the people and businesses that use it. This poses considerable challenges in confirming the plausibility of automatically generated invoices. But mistakes lead to considerable costs and unhappy customers. Can we improve quality by automated plausibility checks?
Choosing how to approach which customers is a daily challenge for all sales representatives. CWS Boco’s sales representatives meet with thousands of potential customers every year and whether or not they convert the opportunity depends on many factors. Type of product sold, trends, time of meeting, the competition and the customer’s industry among many others determine success or failure. Can an AI clear the fog and uncover what truly matters?
Intelligent street lighting is an important step to fulfill the promise of a “smart city”. Smart public street lighting adapts to movement by vehicles and pedestrians and dims when no activity is detected. But which locations promise the most impact by new street lighting? Traffic numbers, weather season, population development, traffic law and other factors have to be considered. Your simulation of 16,000 public street lights might help to introduce “smart” in a smart way!
Every citizen enjoys a clean and tidy city. Therefore, waste bins are placed in most public areas to encourage people not to litter. Waste collection services collect the waste of those bins on a regular schedule. Currently, their route is often based on personal experience of the driver and minimum distances from one bin to another within a specific area. Other factors such as waste bin capacity, local event calendar, vehicle capacity, time of day or weather are not necessarily considered. Can you optimize routes and help keep our cities clean?
Intelligent street lighting is an important step to fulfill the promise of a “smart city”. Smart public street lighting adapts to movement by vehicles and pedestrians and dims when no activity is detected. But which locations promise the most impact by new street lighting? Traffic numbers, weather season, population development, traffic law and other factors have to be considered. Your simulation of 16,000 public street lights might help to introduce “smart” in a smart way!
Every citizen enjoys a clean and tidy city. Therefore, waste bins are placed in most public areas to encourage people not to litter. Waste collection services collect the waste of those bins on a regular schedule. Currently, their route is often based on personal experience of the driver and minimum distances from one bin to another within a specific area. Other factors such as waste bin capacity, local event calendar, vehicle capacity, time of day or weather are not necessarily considered. Can you optimize routes and help keep our cities clean?
Managing a physical real estate portfolio requires a lot of experience and planning. Early indicators can help optimize refurbishment and maintenance planning even without thousands of IoT sensors. Can an algorithm optimize which buildings and apartments should receive significant investment to keep asset value high and tenants happy?
Some municipal utility providers would like to optimize their trading activities. Here, we set our focus to one special contract of the natural gas market, the so-called day ahead. Intraday and short-term price prediction of this specific product can help municipal utility providers to optimize their trading activities with regard to storage operation, balancing group management and portfolio management. Can we design algorithms to forecast intraday and short-term price movements in this context?
To help keep the Ruhr area safe, RAG conducts flights that measure exact ground elevation levels. These measurements reveal ground depressions that can be early indicators of structural collapse and cause property damage in the future. Spotting these depressions is a difficult task. Can we develop an algorithm that spots ground depressions better than the human eye?
Keeping reliable tenants happy is of high importance to landlords. There are many early indicators before a tenant cancels their contract. Their decision might be explained by increasing rent or by complaining about the same issue several times. Can we develop an early warning system for tenants that are about to leave?
Critical infrastructure requires the highest level of data quality. But outdoor sensors are exposed to rough environments and not always deliver the most accurate information. Dirt, aging of sensors and vandalism are typical reasons for data errors. Manual data correction requires a lot of experience, focus and time. It does not scale well with the increasing number of sensors installed. Can smart algorithms come to the rescue?
Choosing how to approach which customers is a daily challenge for all sales representatives. CWS Boco’s sales representatives meet with thousands of potential customers every year and whether or not they convert the opportunity depends on many factors. Type of product sold, trends, time of meeting, the competition and the customer’s industry among many others determine success or failure. Can an AI clear the fog and uncover what truly matters?
Yearly electricity demand is as varied as the people and businesses that use it. This poses considerable challenges in confirming the plausibility of automatically generated invoices. But mistakes lead to considerable costs and unhappy customers. Can we improve quality by automated plausibility checks?
Case: Tenant Happiness :)
Case: Opportunity Scoring
Case: Smart Data Corrector
Case: Spatial Pattern Recognition
Case: Natural Gas Price Prediction
Case: Refurbishment and Maintenance Cycle Optimization
+ Plausibility Check
