To do so, we calculated the impurity-based Gini feature importance48 and visualized it for final analysis. Second, we split the dataset in training set (80%) and test (20%). First, we processed the categorical variables with one-hot encoding before model development and excluded variables with more than 20% data gaps. Thus, the final set of analyzed answers consists of survey entries provided by 64 water utilities. Additional specific sub-questions were displayed depending on the specific answers to the main questions.
These digital tools are already being used for tasks like forecasting renewable energy output, planning grid maintenance, managing vegetation near power lines, and inspecting equipment. The Boston-based company offers the GridOS® software portfolio for grid orchestration. Therefore, it’s unsurprising that 74% of energy and utility companies are embracing AI tools to enhance their operations.
With a staggering 94% of energy service providers acknowledging the struggle to move quickly, it’s clear that the industry needs a strategic pivot. Through a comprehensive restructuring and simplification of its organisational structure, https://lievell.com/7-best-machine-learning-stocks-to-buy-in-2024.html?noamp=mobile Eneco Group has embraced digital tools and digital technologies to streamline its processes. Additionally, the introduction of a versatile workflow engine enabled the seamless integration and customisation of automated procedures throughout the company. This transformation effort is aimed at bolstering essential business functions like customer support and billing. For some companies, it’s even gaining “dragon-class capabilities.” Or it’s a complete overhaul of serving the market with innovative products, services, or business models.
- This is a model designed to triage and socialize ideas and proposed changes across an organization.
- Policy makers should be aware of the possibility that new digital devices and services have the potential to increase energy consumption, for example, as a result of growing quantities of smart household and consumer electronics.
- Furthermore, such common regulation on the installation of digital technologies would certainly encourage the uptake of digital technologies and assert industry decision-makers.
- The digital transformation required meticulous planning and constant monitoring to ensure seamless integration and performance of the new systems.
Optimized grid operations and performance
The addition of pre-posting to this framework begins to elevate the digital awareness and enterprise visibility of the design, resulting in a more intact, though frayed, design fabric as compared to the first framework. The redlines can also be digitally archived in a designated feature data set for historical reference. The reconciliation can occur through ‘truing up’ the data in the GIS or by reconciling the data back in the design tool and pushing it to the GIS. The as-built redlines, which are geographically referenced on a map or geo-coincident, are used to reconcile the as-designed asset geometries and metadata. Utilities must rely on the expertise of their field and back-office teams to ensure the fidelity of their data. As mentioned, certain aspects of this workflow can be digital, but the manual processes of redlining and reconciling the asset inventory and supply chain completely unravel the digital thread and design fabric.
This transformation is not just about technology—it’s about redefining operational strategies, improving workforce productivity, and aligning with evolving regulatory requirements and customer expectations. Utilities that embrace utility digitalization will be better equipped to manage complexity, unlock new value, and deliver reliable, sustainable power to their communities. The authors would like to thank all water utilities that took part in the Smart Water Survey and provided the essential data to develop this study. To identify possible correlations between the utility digitalization progress, measured by UDS, and characteristics of the utilities and the socio-economic context of the countries where the respondent utilities are operating, we developed a model-based feature selection analysis.
- The digital transformation within the utility sector is a multifaceted endeavor, encompassing advancements from automated metering to the integration of complex networking systems.
- The DSO gained 50 percent efficiency and saved EUR 9.44 million over 10 years in capital expenditures.11
- Integrating advanced technologies into utility business models is no longer optional, it’s a strategic imperative.
- The greatest transformational potential for digitalisation is its ability to break down boundaries between energy sectors, increasing flexibility and enabling integration across entire systems.
- As he states, “Grid-wide twins permit similar simulation, asset management, and optimization functionalities as node-specific twins.
- In broader applications like physical security, industrial Ethernet devices are essential for integrating surveillance and monitoring systems across various utility infrastructures.
- At the heart of a utility digitalization is Digital Grid’s advisory and consulting program – a flexible, credit-based service model designed to support utilities and partners throughout every phase of their digital grid journey.
- Defining key performance indicators (KPIs) for each project is crucial for measuring the success of such digital transformation initiatives.
- Continuous training programs help address workforce skill gaps, equipping staff to operate and optimize digital platforms effectively.
- To do so, we calculated the impurity-based Gini feature importance48 and visualized it for final analysis.
Bulk credit discounts, free Trusted Advisor hours, and access to digital training help utilities save money while building internal capabilities. This flexibility ensures that digital transformation efforts can scale with evolving needs. This personalized support ensures smoother communication, faster problem-solving, and a more confident path forward in your digitalization efforts. However, given the poor goodness of fit of the models referred to subdivisions (best R2 coefficients on the test dataset lower than 0.3), only the results for the model referred to aggregate UDS values are reported (Supplementary Figs. 2 and 3). The above procedure for model building, training and validation, and feature importance calculation was replicated both to identify potential predictors of the aggregate UDS across all utility subdivisions, as well as for the UDS of each of the five water utility subdivisions (WS, WD, WW, CD, IT).
Design
Defining key performance indicators (KPIs) for each project is crucial for measuring the success of such digital transformation initiatives. A Swedish water utility installed new technologies of AI-based sensors on intake screens, enabling proactive maintenance and fewer blockages. Through real-time controls and simulation, they identified ways to reduce overflows without huge https://cognifyo.com/articles/solar-energy-storage-nighttime/ infrastructure builds. The utilities industry and energy industry should focus on developing and implementing software solutions that meet their specific needs and requirements.
Analyst Reports
One South Asian utility, for example, cut its percentage of non-revenue water—the amount of water distributed that produces no earnings—by half in https://pluginhighway.ca/blog/how-to-choose-the-best-battery-for-your-tesla-electric-vehicle-and-maximize-its-performance a decade. Breaking this pattern is critical if these utilities are to reduce their financial losses, increase their efficiency, and gain the means to deliver sustainable, high-quality water to their communities. Yet water utilities around the world lose more than 126 billion cubic meters of water annually through leakage and consumption they don’t bill for. Contact Kovaion today to learn how our Oracle HCM Cloud solutions can support your digital transformation journey and drive sustainable growth. Our solutions are designed to help utilities optimize their human resources, improve employee engagement, and foster a culture of continuous improvement.
The oil and gas sector has a relatively long history with digital technologies, notably in upstream, and significant potential remains for digitalisation to enhance operations. These benefits could be all be realised at a limited energy cost, as active controls are projected to consume only 275 TWh in 2040; far less than the TWh they could potentially save that same year. In this scenario, virtually all of the potential benefits of automation are realised, including smoother traffic flow, fewer accidents (vehicles become smaller/lighter) and the widespread adoption of eco-driving and platooning. The interactions among potential disruptions in road transport including the uptake of automated, connected, electric and shared (ACES) mobility will play a key role in shaping the future energy and emissions trajectory of the overall transport sector. Over the coming decades, digital technologies are set to make energy systems around the world more connected, intelligent, efficient, reliable and sustainable. Training and development programs are essential but can be difficult to manage, especially for remote or field-based employees.