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 grids, street lighting and e-mobility-solutions are important steps to fulfill the promise of a “smart city”. Smart public street lighting adapts to movement by vehicles and pedestrians, dims when no activity is detected and is capable of integrating further smart city related functionalities. Public street lights can become charging points for electric cars for instance. But which locations promise the most impact? Traffic numbers, electric vehicle (EV) numbers, weather season, population development, traffic law, demographic factors, power grid capacity and other factors have to be considered. Your simulation of the grid might help to introduce “smart” to a smart city!
Every citizen enjoys a clean and tidy city. While regular waste from bins is collected following optimized routes, pick-up of bulky garbage & electronic waste, like old couches, broken TVs or shelves does not follow a regular schedule. Citizens request for pick-up on a specific day. Currently, the collection of bulky waste & electronics are optimized manually on a daily basis by personal experience of the driver and minimum distance from one location to another within a specific area. Other factors such as vehicle capacity, time of day, whether or construction sites are not necessarily considered. Can you optimize routes and help to keep our cities clean?
Intelligent grids, street lightingand e-mobility-solutions are important steps to fulfill the promise of a “smartcity”. Smart public street lighting adapts to movement by vehicles andpedestrians, dims when no activity is detected and is capable of integratingfurther smart city related functionalities. Public street lights can become charging points for electric cars forinstance. But which locations promise the most impact? Your simulation of the grid might help to introduce “smart” to a smart city!
Every citizen enjoys a clean and tidy city. While regular waste from bins is collected following optimized routes, pick-up of bulky garbage & electronic waste, like old couches, broken TVs or shelves does not follow a regular schedule. Citizens request for pick-up on a specific day. Currently, the collection of bulky waste & electronics are optimized manually on a daily basis by personal experience of the driver and minimum distance from one location to another within a specific area. Other factors such as vehicle capacity, time of day, whether or construction sites are not necessarily considered. Can you optimize routes and help to keep our cities clean?
Intelligent grids, street lighting and e-mobility-solutions are important steps to fulfill the promise of a “smart city”. Smart public street lighting adapts to movement by vehicles and pedestrians, dims when no activity is detected and is capable of integrating further smart city related functionalities. Public street lights can become charging points for electric cars for instance. But which locations promise the most impact? Traffic numbers, electric vehicle (EV) numbers, weather season, population development, traffic law, demographic factors, power grid capacity and other factors have to be considered. Your simulation of the grid might help to introduce “smart” to a smart city!
join networkIntelligent grids, street lighting and e-mobility-solutions are important steps to fulfill the promise of a “smart city”. Smart public street lighting adapts to movement by vehicles and pedestrians, dims when no activity is detected and is capable of integrating further smart city related functionalities. Public street lights can become charging points for electric cars for instance. But which locations promise the most impact? Traffic numbers, electric vehicle (EV) numbers, weather season, population development, traffic law, demographic factors, power grid capacity and other factors have to be considered. Your simulation of the grid might help to introduce “smart” to a smart city!
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?
