C3 Energy’s family of utility-tested and proven products deliver end-to-end solutions across the entire smart grid, from energy generation, transmission, distribution, and advanced metering to the customer experience. C3 Energy’s Smart Grid Analytics solutions enable utility operators to realize the full economic, social, and environmental promise of the smart grid and modern energy systems. C3 Energy’s suite of analytics applications unlock up to $300 per meter in annual economic benefit for utility operators and energy consumers.

  1. Improved safety due to early identification of problem meters
  2. Robust smart meter network and data quality, enabling greater capture of benefits from additional or enhanced data analytics, which otherwise could not be performed
  3. Increased customer satisfaction through improved diagnostic of problems with smart meters
  4. Consistent reporting on smart meter deployment for contractual and regulatory reporting purposes
  1. Pinpoint patterns in load profile and event data streams that identify revenue leakage or maintenance and safety issues
  2. Identify correlations from multiple utility systems (AMI, Customer, Billing, OMS, External)
  3. Identify highest revenue theft opportunities and streamline assignment of opportunities
  4. Continual data mining of the outcomes of closed opportunities improves the ability to hone analytical accuracy, increasing the success rate
  1. Proactive, real-time assessment of asset health, along with condition-based maintenance
  2. Increased detection of assets at risk through asset lifecycle monitoring
  3. Early identification of network vulnerabilities.
  4. Customer view of the impact of assets at risk (e.g., segment, revenue, satisfaction)
  1. Enterprise-wide framework to effectively evaluate capital investment alternatives and strategically assess competing priorities
  2. Ranked grid investment portfolio alternatives based on cash flow analysis, impact to system, and customer reliability
  3. Informed business cases built for asset replacement
  4. Reduced operations and maintenance (O&M) expenses and optimized capital expenditures from directing investments to circuits at greatest risk of outage
    • Performance monitoring through comprehensive metrics
    • Benchmarking to assess performance relative to peer group
    • Recommendations on short and long term energy and capacity bids
    • Optimized dispatch to meet Reliability Must Run (RMR) requirements
    • Metrics to facilitate near real-time evaluation of outage performance
    • Optimized and reported outage scheduling decisions for near real-time decision requirements
    • Quantified potential impacts of outage scheduling on operations, maintenance, market / dispatch, financials
  1. Accurate short-, medium-, and long-term electricity load forecasting across commercial, industrial, public sector, and residential customers at the meter, residence, facility and appliance, and equipment level
  2. Load forecasts based on advanced machine learning techniques, including non-linear non-parametric techniques for time series prediction
  3. Granular predictions of load with computation at the meter and disaggregation to the appliance level, as opposed to substation or feeder level
  4. Refined and improved load forecast accuracy with customer-supplied information about appliances, energy use, and energy efficiency actions provided through C3 Energy Customer Analytics™
  1. Prediction of load from hourly to yearly timescales by analyzing patterns of energy use from individual meters up to large aggregations of customers, combined with external data such as weather, economics, and energy/ technology price trends
  2. Analytical foundation for more efficient capital and operational planning
  3. Identification of high-risk areas of the grid to proactively target
  4. Robust customer analytics and segmentation
  5. Load forecasts for individual premises and appliances
  1. Pinpointed restoration priorities with greater speed and accuracy in near-real time
  2. Visual display of crew locations, status, and shortages
  3. Valuable outage and restoration insights from investments made in AMI infrastructure
  4. Improved internal and external communication precision and delivery
  5. Continually updated restoration time and reliability metrics
  6. Hour-by-hour playback to learn, react, and prepare for storm and crisis situations
  1. Proactive identification and assessment of feeders through which voltage and reactive power can be reduced
  2. Visual identification of voltage optimization opportunities based on dynamic display of inefficient feeders
  3. Customer analysis of the voltage optimization opportunities (e.g. customer segment, type, revenue, satisfaction)
  4. Assessment of estimated cost to implement and operational savings from reactive power and voltage reduction
  1. Real-time monitoring, visualization of IT + OT network state and vulnerability / threat trends
  2. Cyber threat diagnosis, root cause context for analysts and responders
  3. Prioritized recommendations based on energy delivery impact, reliability, customer service
  4. Intuitive visualization tools for diagnosis, exploration of vulnerabilities
  5. Detailed displays of risk indices and contributing factors
  1. Analyze energy usage and costs and compare to similar homes
  2. View disaggregated end-uses of energy and learn about ways to save
  3. Create a personalized energy savings plan
  4. Save money and earn rewards by saving energy
  1. Analyze energy usage and costs and compare to similar businesses
  2. View disaggregated end-uses of energy
  3. View tailored energy saving tips
  4. Build a personalized energy savings plan
  1. View in-depth customer energy profiles and analyze facility details and energy usage trends
  2. Create energy forecasts viewable at every level, from sub-meter to building to city to region.
  3. Track, compare, and benchmark multiple facilities with a searchable catalog of facility details
  4. Optimize a portfolio of energy projects into an actionable plan
  1. Engagement in less than five clicks, encourages customer self service, and reduces call center volume
  2. Details on bill drivers and different rates
  3. Text or email alerts related to billing and usage, demand response, outage, or marketing applications
  4. Moving assistance to start, stop, or transfer service
  5. Service outage support to view outages and planned service interruptions
  6. Comparison of energy consumption to an average and efficient home or building
  1. Ensure higher return on marketing investment by marketing products and services that are targeted to the unique needs and attributes of each customer segment
  2. Move from large, broad-based, inefficient marketing programs to smaller, more agile, higher-yield campaigns
  3. Create and update product, service, and program offerings to ensure that the unique needs of customers are met
  4. Build compelling campaigns that quickly and easily deliver relevant, personalized, and time-sensitive communications to customers through their preferred communication channel: web, email, mobile, social networks, and print.
  5. Manage marketing assets, programs, and campaigns in one single, end-to-end marketing solution.
  1. Transparency of program progress and its energy savings calculations
  2. Defined Data Inputs & Data Cleansing: improved quality of data prior to EM&V analysis, correcting for issues such as missing data, zero data, and anomalous usage values
  3. Refined Algorithms: EM&V algorithms run to evaluate, measure, and verify the key success parameters
  4. Refined Control Groups: control groups created, and selection verified and compared
  5. Automated Results: results of program participation metrics including opt-in, opt-out, percentage of online users, average energy consumption per participant, average energy savings per participant, average savings rate per participant, and applicable T-statistics
  1. 360° view of customer interactions through all service channels, enabling managers to improve CSAT and reduce costs
  2. Tracked key operational metrics, e.g., costs against budget, operator utilization, interaction reasons and channel trends, customer satisfaction
  3. Detailed view of customer interaction sequences and trends, linked across service channels
  4. Identified root causes (reasons) for customer interactions and predicted best resolution paths
  5. Analysis of key patterns of interactions by demographic and customer service segment
  1. Determine probability of failure of drilling operations
  2. Pinpoint the root-cause of potential failure down to the specific rig equipment
  3. Find the optimum drilling settings by running near real-time analytics on drilling data signals (e.g., WOB, ROP, RPM, mud volume, cuttings)
  4. Send maintenance work orders directly to the workflow management system
  1. Develop optimal well placement plan to enable maximum EUR across an entire field to be developed
  2. Analyze recommended completion parameters, including perforation interval, proppant size, and fracturing fluid volume
  3. Predict well performance of various completion design and fracturing techniques
  4. Improve field development plans by incorporating the latest insights and completion data from recently completed wells
  1. Proactively assess real-time health of wells, quickly prioritizing wells that require intervention
  2. Understand the potential economic, safety, and environmental impact of an incident
  3. Pinpoint the root-cause of potential failure (e.g., tubing failure, plugging, casing hanger seal failure, annulus valve failure)
  4. Improve work planning by identifying and prioritizing wells that require intervention
  1. Optimize secondary production parameters across the entire field based on bottom-up, individual well analysis
  2. Build custom oilfield scenarios including the ability to adjust injection rates and lift rates on a per well basis
  3. Predict field-wide and individual well production performance
  4. Integrate intelligence into future field design plans to improve production rates, efficiency and safety
  1. Proactively assess real-time asset health, along with failure predictions, maintenance expense projections, and potential capital expenditures
  2. Understand the impact that a high-risk asset has on production, reliability, safety, and environmental goals
  3. Create maintenance work orders to quickly mitigate potential equipment issues
  4. Reduce capital expenditures by identifying at-risk equipment and flagging for inclusion in investment planning projects
  1. Evaluate capital investment alternatives and strategically assess competing priorities (e.g., asset risk scores, production rates, HSE impact)
  2. Rank investment portfolio alternatives based on different financial metrics (e.g., payback period, NPV, IRR, cash flow analysis)
  3. Aggregate multiple, detailed plans into higher-level capital plans
  4. Track and measure performance against plan, by comparing actual spend on projects to forecasted costs
  1. Quickly recognize instances of potential oil and gas loss due to leaks or theft
  2. Identify loss details and launch work orders directly from application
  3. Measure, validate, reconcile and report on oil and gas volume flows and inventories from production to transportation, storage, receipt, and delivery
  4. Increase the long-term accuracy of hydrocarbon loss with detection algorithms that leverage machine learning techniques
  1. Predict demand across the value chain by analyzing oil and gas consumption patterns with external data (e.g., weather, historical energy price trends)
  2. Evaluate margins on a per well basis and develop recommendations for production rates
  3. Forecast optimal production levels and product mixes for refineries to match fluctuating demand
  4. Analytical foundation for more efficient capital and operational planning
  1. Monitor and optimize refinery production units by aggregating data sources from across the refinery portfolio
  2. Increase production and reduce operating expenses as a result of increased visibility of control processes
  3. Continually adapt refinery unit production model with changing internal and external factors
  1. Aggregate financial, operating, and marketing data across portfolio of fuel stations
  2. Decrease operating costs by benchmarking fuel station performance
  3. Increase same store sales by developing marketing program recommendations
  1. Ensure higher return on marketing investment by promoting products and services that are targeted to the unique needs and attributes of each customer segment
  2. Move from large, broad-based, inefficient marketing programs to smaller, more agile, higher-yield campaigns
  3. Create and update product, service, and program offerings to ensure that the unique needs of customers are met
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