Advantages of Predictive Analytics For Utilities

Predictive Analytics Solutions for Utilities

The infrastructure of public utilities is huge, so there are many potentially vulnerable places in it. Is it possible to prevent accidents, protect people from discomfort and save companies from losing money? Predictive analytics for utilities is an effective solution in this case. Let’s look at what benefits forecasting-based IT solutions can bring to your business.

Advantages of predictive analytics solutions for utilities

Utility companies, like other large advanced organizations, promptly implement IT solutions. This facilitates the process of servicing consumers and monitoring the status of important assets. As a rule, such applications are based on comparing fresh and historical data using machine learning algorithms. The model analyzes data on assets and predicts their possible condition in the near future.

Software with predictive analytics changes the work of utilities for the better, namely:

Improve customer service.

Based on predictive analytics, utility companies can anticipate failures and prevent them, or at least warn consumers. 

Utility companies also learn about the needs and intentions of customers. For example, if a consumer abruptly changes his/her behavior model, this may signal a strong desire to leave for a competitor. The system warns your company about this, and you can offer users the appropriate services. This approach increases customer satisfaction and reduces the chance of their departure to competitors.

Analytics can also be used to determine which customers are more likely to respond to an ad. For example, one utility company used artificial intelligence to identify users who advocate for environmental protection. The organization offered them to switch to an electronic billing system and saved about two million dollars.

Do not overpay for maintenance.

Scheduled maintenance of systems is cheaper than urgent repairs. Organizations do not overpay for processing in emergency situations, lawsuits and fines from regulatory authorities and do not lose revenue. Equipment that is regularly checked works better and runs longer.

Risk management panels, heat maps, and IoT devices signal that thresholds and anomalies are exceeded. The programs offer options on how to protect your facilities from accidents.

Protect employees.

A suddenly broken pipe or wires are dangerous both for employees of the utility company and for local residents. Solutions with predictive analytics include weather data and suggest at what time it is better to repair engineering structures.

Prioritize tasks.

A machine learning system evaluates assets and the degree of risk in case of failures. Using this information, it is easier to prioritize tasks and direct personnel to those areas that need repair in the first place.   

Identify scammers.

If a utility asset is not working properly, this may indicate illegal use of services. Predictive analytics helps to detect such anomalies. 

Optimize supply and demand.

Predictive analytics, together with IoT devices, provides accurate statistics on the use of water and electricity, as well as optimizes supply and demand.

Fixes problems with paying bills.

Predictive analytics compares the payment behavior of customers. Thus, utility companies will find out which customers pay for services on time, and who is experiencing financial difficulties and unable to pay bills. This is how companies find vulnerable customers and can respond to problematic situations in a timely manner.

Problems of implementing predictive analytics

When implementing solutions that use predictive analytics, your business may face four main problems.

Difficulties with processing big data in energy and utilities.

For predictive analytics to work effectively, your company must have a structured data archive. However, in practice, things are often different — the information may be poorly organized.

If your team has worked with several asset management programs, the data may be scattered across different sources. But before implementing forecasting tools, they need to be integrated.

The information may be partially missing, which also violates the calculations. But any company developing special software will definitely help you choose the appropriate information to create an accurate forecasting model.

Implementation costs.

Implementing predictive analytics is an expensive process, but the long-term benefits outweigh such investments. Over time, there will be fewer accidents and unplanned outages at the enterprise. In addition, the assets will last longer.

Choosing a technology partner.

Successful solutions using predictive analytics should be focused on a specific business. This will allow you to process the information correctly from the very beginning and give accurate forecasts. At the very least, the program should process large volumes of structured and unstructured data, as well as integrate with external statistical tools.

External factors.

It is impossible to predict how external factors will affect your company’s assets. Depending on weather conditions and geographical location, equipment at one enterprise may wear out earlier than at another.

Predictive analytics software does not know what external factors will affect assets, but it can track changes in their performance and assume failures.

Conclusion

Predictive analytics for utilities saves you time and money by ensuring stable operation of production assets. The equipment runs longer, and customers do not suffer from any kind of accidents.

If you need to develop software for corporate asset management and expense accounting, feel free to contact Andersen. Our specialists have sufficient experience in creating digital solutions. Using predictive analytics allows our team to successfully transform your business.