Baseline year
All you need is NEM12 data to start your energy flexibility analysis.
Understand consumption and costs.
Perform data transformations, calculate tariffs, maximum demand and emissions factors quickly and accurately! Deep Energy calculates outputs from utility data interval by interval utilising machine data processing and algorithms.
Accurately calculate consumption and demand, interval by interval.
With ease.
Import 5, 15, 30-minute interval, or daily data in CSV format into a standard import template and seamlessly upload it to the database.
- NEM12 data in 5, 15, or 30-minute intervals is processed with peak power automatically calculated in KVA from the source Wh and VARh.
- Rolling maximum demand with different time windows and seasonality applied from the tariff engine.
- Emissions factor calculations quantify reductions in emissions for sustainability and carbon credit schemes.
- Leverage other integrated data sources such as weather, tariffs, and spot price of electricity.
Accurate data forms the basis for quality modeling results.
- Accurate tariff calculations for bill validation and cost projections.
- Validate the baseline year costs against actual invoices.
- Flexible load disaggregation from baseload using k-means clustering algorithm to quantify load-shifting and reduction opportunities.
- Calculates maximum demand per rolling window and interval to evaluate demand reduction opportunities of flexible load.
- Advanced scenario modeling all require accurate calculations, interval by interval.
A source-of-truth from utility data
Extract, transform and load processing millions of records of utility data in a few clicks.
Create a baseline from site, billing and tariff data.
Leverage machine processing and algorithms to produce a source-of-truth for modelling and energy flexibility analysis.
- Data transformation
A source-of-truth from data. Obtuse meter data formats are easily processed by Deep Energy into usable data for creating CenterFocusWeak into energy usage greater than that of your utility company.
Convert 5, 15 or 30-minute interval data in CSV format into a standard import template and seamlessly upload to the database.
Data validation processing engine eliminates error, ensuring data quality for modelling.
Emissions factor calculations quantify reductions in emissions for sustainability and carbon credit schemes.
Resultant data are stored in the Data Clearing House time-series database for use with other data sources such as weather, tariffs and spot price of electricity. - Load disaggregation
Behavioural analysis underpinned by machine learning and genetic algorithms discovers variable electrical loads – loads that do not conform to any known schedule or regular pattern.
Flexible load disaggregation from baseload using k-means clustering algorithm to quantify load-shifting and reduction opportunities.
Initial (or base-load) is considered to be inflexible loads; whereas
flexible load is the component of load that shows variability.
Identify the flexible loads in your clients’ energy systems to better manage and optimise energy usage. - Maximum demand
Understand the maximum demand opportunity. Easily and accurately calculate maximum demand per rolling window and interval to evaluate demand reduction opportunities of flexible load.
Two years of data is required to calculate rolling 12-month maximum demand figures = to 630,720 observations for 5-minute interval format.
The tool then sums highest demand interval with the corresponding intervals in the interval length variable per day and the highest per rolling window.
The output of maximum demand is displayed visually to the user in a time-series format when a baseline or what-if scenario is run.
Spot outlier events in conjunction with flexible load, drill into outlier days for more detail. Understand the maximum demand opportunity. - Tariffs
Each network and retailer have their own names and structures for charging electricity which makes tariff analysis hard to understand, seemingly by design. The tariff engine calculates charges interval by interval and normalises line items into a consistent schema for:
Charge breakdowns and billing validation.
Analysing the cost of energy in the forecast year and into the future.
Comparing alternative retail, network and/or wholesale tariff options. And;
Using baseline energy costs to model other alternative scenario options. - Focus your strategy
Design a strategy from your source-of-truth baseline to realise objectives.
Now you have your baseline… Do you want ROI? Determine the lowest cost options? The business case for the wholesale market? The optimal energy technology mix? How to reduce operational costs and emissions?
Create unlimited scenarios, copy, edit and apply them to other sites or clients. Apply sensitivities, best, likely and worse case scenarios at the assumption or scenarios level with ease. Avoid tedious aggregation and calculations in building scenarios or in applying scenarios to other projects, improve efficiency, and reduce time-to-analysis, error and risk.
- Data transformation
A source-of-truth from data. Obtuse meter data formats are easily processed by Deep Energy into usable data for creating CenterFocusWeak into energy usage greater than that of your utility company.
Convert 5, 15 or 30-minute interval data in CSV format into a standard import template and seamlessly upload to the database.
Data validation processing engine eliminates error, ensuring data quality for modelling.
Emissions factor calculations quantify reductions in emissions for sustainability and carbon credit schemes.
Resultant data are stored in the Data Clearing House time-series database for use with other data sources such as weather, tariffs and spot price of electricity. - Load disaggregation
Behavioural analysis underpinned by machine learning and genetic algorithms discovers variable electrical loads – loads that do not conform to any known schedule or regular pattern.
Flexible load disaggregation from baseload using k-means clustering algorithm to quantify load-shifting and reduction opportunities.
Initial (or base-load) is considered to be inflexible loads; whereas
flexible load is the component of load that shows variability.
Identify the flexible loads in your clients’ energy systems to better manage and optimise energy usage. - Maximum demand
Understand the maximum demand opportunity. Easily and accurately calculate maximum demand per rolling window and interval to evaluate demand reduction opportunities of flexible load.
Two years of data is required to calculate rolling 12-month maximum demand figures = to 630,720 observations for 5-minute interval format.
The tool then sums highest demand interval with the corresponding intervals in the interval length variable per day and the highest per rolling window.
The output of maximum demand is displayed visually to the user in a time-series format when a baseline or what-if scenario is run.
Spot outlier events in conjunction with flexible load, drill into outlier days for more detail. Understand the maximum demand opportunity. - Tariffs
Each network and retailer have their own names and structures for charging electricity which makes tariff analysis hard to understand, seemingly by design. The tariff engine calculates charges interval by interval and normalises line items into a consistent schema for:
Charge breakdowns and billing validation.
Analysing the cost of energy in the forecast year and into the future.
Comparing alternative retail, network and/or wholesale tariff options. And;
Using baseline energy costs to model other alternative scenario options. - Focus your strategy
Design a strategy from your source-of-truth baseline to realise objectives.
Now you have your baseline… Do you want ROI? Determine the lowest cost options? The business case for the wholesale market? The optimal energy technology mix? How to reduce operational costs and emissions?
Create unlimited scenarios, copy, edit and apply them to other sites or clients. Apply sensitivities, best, likely and worse case scenarios at the assumption or scenarios level with ease. Avoid tedious aggregation and calculations in building scenarios or in applying scenarios to other projects, improve efficiency, and reduce time-to-analysis, error and risk.
