Meniscus Energy Blog – Meniscus

New makeover for the original MCE Analytics Engine!

meniscus — Tue, 15 Feb 2022 21:37:27 +0000

New AngularJS webclient revitalises our original MCE Analytics Engine – low-code analytics for developing solutions easily and quickly

We are very pleased to announce that our original MCE analytics engine has had a bit of a makeover! A new AngularJS webclient and updated Javascript widgets have revitalised the analytics engine. It is just as fast, just as flexible and just as easy to use. However, the new AngularJS dashboard makes it much easier to set up, configure and modify Items. The new Javascript widgets also make it easier to create your own dashboards and to display the metrics you need.

Request a demo of MCE

The principles behind MCE are becoming more accepted as more companies appreciate the benefits that self-service access to data and associated analytics within the organisation. MCE has always focused on enabling anyone in to quickly develop a simple application, and then add more complex analytics as they understand both the capabilities of MCE and as the application itself develops. The new New AngularJS webclient now makes it a whole lot easier to use!

Traditionally, most users of MCE are based in the energy or water sectors, using MCE to calculate complex energy tariffs; energy management; pumping efficiency metrics; chemical consumption and dosing; spill monitoring and prediction for water assets; real-time home-energy monitoring to name a few. However, we believe that MCE is a powerful and flexible tool for any organisation that wants to enable its workforce to deliver real-time analyitcs solutions without having to rely on an army of data scientists to create the background analytics.

Some case studies can be found here.

MCE analytics engine infographic

Meniscus Calculation Engine – MCE (low-code analytics engine)

MCE Javascript Widgets – built using MCE AngularJS webclient

More information on Operational Analytics

The post New makeover for the original MCE Analytics Engine! appeared first on Meniscus.

MCE widgets update is now available

meniscus — Tue, 30 Nov 2021 14:11:58 +0000

New MCE widgets update is now available. Makes it even easier to build and display your own analytics

Our new MCE widgets update is now available. This lets users build their own dashboard solutions. Using the widgets, you can build your own dashboards and select the way that you want to display your data. Key features in the new widgets include:

New types of widget: Pie chart, Sparkline chart, Scatter chart and Gauge

Ability to aggregate data directly in the widget

Improved dashboard creation code. Easier to size the widgets how you want and edit the titles

Migrated the whole widget codebase to AngularJS, so, it will be much easier to extend in future

The MCE widgets build on the existing MCE low-code analytics platform and are designed to give users simple access to their data.

This is an example of an iframe built using the new Gauge widget code. Electricity values are updated every 6 seconds. The widget updates every 2 minutes.

For more information on MCE widgets

For more information on Meniscus Calculation Engine (MCE)

What is a widget?

The post MCE widgets update is now available appeared first on Meniscus.

Home Energy platform upgrade – new Widget graphs

meniscus — Sun, 05 Jan 2020 16:38:11 +0000

Home Energy platform upgrade lets users create their own dashboards

We have upgraded the Meniscus Calculation Engine (MCE) on our free-to-use Home Energy monitoring platform. We are phasing out of the existing Silverlight dashboard as it only runs on Internet Explorer over the next couple of months.

Example of an MCE real-time widget

This graph will update every 2 minutes.

New Widget graphs and getting your API key

The widgets let you easily create your own dashboards that you can configure yourselves.

To use the widgets you will need your API key which is the first 16 characters of the email address that you used to create your account with us. Please note a couple of things.

THE API key is READ ONLY

if you make these widgets publicly available then your API key will be visible

If you would like to change your API key please send a mail to sales@meniscus.co.uk and quote the e-mail address you use for your Home Energy account

To create the widgets

Go to the widget page
In the “Create Widget – Enter API key” tab enter your API key
Click Connect to Server
In the “Create Widget – Meter Options” tab select the Items you want to display

For the Real time data use Electricity use – channel 1. This is the RAW data type with units of W
For the Half hour aggregated data for the three main channels then use Electricity use – channel 1 – HH. This is the CALC aggregated W data converted into kW
You can add any other Item that you want to display

Select the Output type – I.e. line graph
Add Item to the List
In the “Create Widget – Date and Time options” tab select the time option you want. Don’t use too long a period as it will take a long long time to upload!
In the “Create Widget – Update Options” tab select how often you want the widget updated – so for a two minute update enter 120 seconds

When you are ready – select the Output HTML. This will generate the iframe code that is can be pasted into any HTML web page to generate the graph displayed.

Create the Dashboard

Follow these steps to create an HTML dashboard with the widgets you have created. This will give you a lot more visibility for the Home Energy platform.

When you have created the Output HTML that you want click the “Create Dashboard” tab

Click on “Add Widget” and copy the HTML iframe. This will create a new widget and you can add up to 4 different widgets on the one page and move them as you want

Click on “Add Header” to add a simple header to the page

Click on Copy HTML to create a copy of the dashboard as an HTML page. If you open this page then you can view the widgets you have created

For more information on the MCE widgets click here

See an example of the MCE widget dashboard

The post Home Energy platform upgrade – new Widget graphs appeared first on Meniscus.

DUoS charges – How will DCP 228 affect you??

meniscus — Mon, 30 Oct 2017 15:00:05 +0000

DCP 228 – the name of the upcoming regulatory change brought in by OFGEM – will see a modification to the way electricity distribution charges are calculated.

The post DUoS charges – How will DCP 228 affect you?? appeared first on Meniscus.

Multiple Regression Analysis Lowers Fleet Costs

meniscus — Tue, 30 Aug 2016 13:08:58 +0000

Do you have a fleet management system? Does this calculate how much fuel is being consumed by each vehicle and by whom? This is very important information and very often nothing, or not enough, is being done with your information.

With the constant fluctuation in fuel prices, it can be difficult for transport companies to budget their fuel costs as accurately as possible. Many factors contribute to the total fuel cost.

Multiple Regression Analytics lower fleet fuel

Lowers Fleet Costs

Meniscus has used these factors to model the cost of fuel for transport companies. Through Multiple Regression Analysis, an 11% reduction in total fuel costs could be achieved, by reducing the number of idling hours of drivers and their harsh driving scores by 20%.

Multiple Regression Analysis allows a correlation between a dependent variable and many independent variables. The analysis provides the coefficients for each variable giving an equation in the format of y = β₀+ β₁X₁+ β₂X₂+…+β_nX_n. From the established regression equation, it is then possible to use the independent variables to estimate the dependent variable, in this case, fuel cost. Multiple Regression Analysis also allows us to determine the contribution each individual independent variable makes to the dependent variable value. We are in the early stages of analysing transport data and believe it is a sector that would see real benefit from our MCE (Meniscus Calculation Engine).

We are not saying you shouldn’t use the system you have. All we are saying is that maybe we can do more with data and help you become even more cost efficient. In other words, get more from your system and by analysing the data produced to reduce costs, giving you more time to focus on other pertinent areas of your business.

Watch this space for new amazing on developments.

The post Multiple Regression Analysis Lowers Fleet Costs appeared first on Meniscus.

The Unspoken, Gaps in Data

meniscus — Thu, 18 Aug 2016 13:26:38 +0000

Over the years we have come across a number of problems when it comes to receiving data. One of the common problems is where there is are gaps in data. This can cause problems for the energy management system that you may be using, and make your job even more difficult and time-consuming than it already is.

When there is no data. What do you do?

The most used and straight forward method is to carry forward values. Meaning you’re taking a previous value and bringing it forward to fill the gap. This can work when you have a single value missing and it may be sufficient depending on the scope of your needs.

Unfortunately, there is no right or wrong method of filling gaps, as it is always a compromise. The method you choose is dictated by the quality of the data you require versus the effect to attain it and the importance of the impact that the choice of the method makes to your results.

Using the MCE (Meniscus Calculation Engine) we have come up with a system that allows us to extract the data from your Energy Management System (or any other system that you may use) and pull it into MCE. This is where we set up calculations that automatically identify the data gaps.

There are various different methods filling gaps in data and there is no right or wrong method of filling gaps, as it is always a compromise. The following methods are just some of the ways you could fill the gaps in data:

Carry values forward. Meaning you’re taking the previous value and bringing it forward to fill the gap.

You could use data that was received at the same time. Data from a day, week or year before, and use that to fill the gap.

Other more complicated methods involve the following:

Taking averages of points in previous years for the same point. Or doing more with season + day type averages. Each day has an associated day type (weekend + weekday for a simple example) and season of the year. This allows averages to be built that are more reflective of seasonal variations and usage and are less prone to suffering from the impact of more extreme individual values.

Back-filling data by Interpolation using incrementing data sets. For example, if your incremental counter ended on 100 units at the start of the gap and started on 180 units at the end of the gap that means we used 80 units. And, 80 units is over a 4-day period, so therefore it’s 20 units per day. This we feel to be the most accurate method as the data is there and is accurate as it is actually what has been utilised.

Using MCE (Meniscus Calculation Engine), we extract the data into our systems where the calculations are set up to automatically identify the data. E-mailed Management reports are generated by MCE and the information is sent to you or directly to the Data Collector to ensure that the gaps are filled with the correct data.

Mind the Gap

The post The Unspoken, Gaps in Data appeared first on Meniscus.

Process Based Energy Management for the Water Industry

scaadmin — Sat, 10 May 2014 09:00:06 +0000

1. Introduction
This White Paper sets out a methodology for using the Meniscus Calculation Engine (MCE) as a bottom up process centric Energy Monitoring and Targeting based application for use in the Water Industry.MCE is a generic application and so can be used for the monitoring of any type of numeric based data and for setting up set of calculations required to understand the performance of assets.

2. Methodology

2.1. Setting up a Process Taxonomy

Each Water Company has its own way of managing their asset base so as to ensure that naming conventions related to the myriad pumps, blowers, drives that comprise a water or wastewater asset have a unique asset identification tag.In developing this form of asset database, each company will normally have a taxonomy for classifying the purpose of these assets which generally is process centric. I.e. the assets are related to some physical process such as; Inlet wastewater pumping etc.Our recommendation in setting up an energy based (also applies to chemical monitoring) is to use the taxonomy structure already used by the Water Company and to match the database structure to this asset taxonomy.

2.1. Bottom up monitoring
Many Energy Monitoring and Targeting applications are top down driven, i.e. from the fiscal electricity meter installed for the Site and then down, possibly one layer, to any sub meters installed on site. This is because these applications require the use of metered electricity data.Unfortunately the information gathered by such monitoring is limited since sub meters are expensive to install and, in the UK, few sites have comprehensive sub metering at process and sub process levels.Using MCE, the customer can adopt a bottom up approach by maximising the value from existing data that is frequently available from site, namely hours run data for the fixed speed pumps and drives and converting this into consumption data using the rated kW power demand for each drive. By using this very granular data (often available at 15 minute periods) it is possible to build up a very good approximation of energy use at sub process and then process level and to be able to set and apply process centric targets. This process level energy data can then be aggregated up to Site level where it can be compared to the actual fiscal electricity use. This gives a good understanding of the accuracy of the overall âmodelâ developed for your site.

2.3. Data Accuracy
There are obvious limitations to this approach and so in practice it is important to understand and then workaround these limitations.

1. Variable speed drives. This is not an approach that can be used with variable speed drives (VSD) and sub metering is realistically the only alternative if there is a sub process or process making heavy use of variable speed drives. Experience shows that VSDs are either used on large pumps and blowers (wastewater inlet pumps, water distribution pumps, aeration blowers etc) where the energy consumption may justify sub metering, or on small precision drives (sampling pumps, chemical dosing pumps etc) where consumption is minimal and can be ignored.

2. Fixed speed drives that are intentionally over-rated. Some wastewater processes will have drives installed that are over-rated for normal operation. Whilst this may represent an inefficiency, they are installed where they may be called to deliver larger load at specific times. Examples include; Wastewater inlet screw pumps (Archimedes screws) and aeration rotors/surface aerators. In these cases, and in any case where the validity of the kW rating of the drive is questioned, then the current drawn should be used instead.

3. kW, kWh and Current data form HMI panels. Newer electricity distribution panels are frequently installed with energy monitoring capability already built in and displayed on local HMI panels on the distribution board. Where such data is available then this should be used instead of any hours run data. In this way, over time the reliance on less accurate hours run data will become smaller and smaller.

2.4. Real Time vs Historic
There is a continual discussion concerning the merits of near real time vs historic monitoring. When using MCE this discussion is not that relevant since the system can deliver both modes of operation. Though large volumes of real time energy or hours run data will impact on server performance.The issue is more a question of what will be done with the information once the system is up and running. If your company has the ability to act on the larger volume of alerts coming from a near real time system then fine but experience indicates that this is frequently not the case (many companies do not want energy based alarms included in their main control room responsibilities) in which case the processing of D+1 data may be more appropriate. D+1 means that yesterdayâs data is received and processed early this morning. The raw data is still processed at 15 minute intervals â but the overall calculations are delivered in time for operations to act on them early the next day.

2.5. Data Cleansing
Data cleansing is an essential element of any data monitoring task and must be considered as part of the development.Energy data itself is frequently of good quality but the area where data cleansing is required is in the normalising factor when creating benchmarks and performance KPIâs. This normalising factor is often flow based (i.e. kWh/Ml, kWh/m3 etc). Examples of data cleansing issues to consider include:

1. Negative values. Frequently seen with flow meters caused by errors in the set up of the flow meter. Look to resolve the issue with the meter but ignore these negative values using an IF statement â IF flow < 0 THEN 0 ELSE calculation

2. 0 values. Whilst 0 values may be correct you will generate an error since they cause a divide by 0 error. Remove by using a IF Statement â i.e. IF flow = 0 THEN 0 ELSE calculation. Look to combine error 1 and 2 into the same calculation expression.

3. Automated removal of outliers. Used in a range of target setting application when you want to select a particular set of data for automated target setting and need to ensure that any outliers are excluded from the dataset. Create calculations based on deviation above and below standard deviation values â I.e. ignore anything where the value < or > mean +/1 2/3 standard deviations. Can use a REMOVE function to ensure that any mean or standard deviation calculation ignores values of 0

2.6. Process based Targeting âhistoric or model based?
The purpose of any Monitoring and Targeting system is to identify the key normalisers that control energy use and set the targets around these.For a range of Industrial/Commercial applications this is relatively simple since the normalisers for monitoring energy use in buildings and in some production environments are quite simple. With Water and Wastewater assets then these normalisers are more complex and in some cases may not be available/suitable to be monitored â i.e. Biological Oxygen Demand as the normaliser for the aeration process in a wastewater treatment plant.As such the setting of targets becomes more complex and may require the use of more than 1 independent variable and the variables themselves may be surrogates for the variable that we really wish to monitor.The purpose of setting this target is to be able to identify when the actual sub process and process performance deviates away from this target.An example of the relationship between the Actual and Target use for a large wastewater treatment plant is shown in Appendix 7.1. This data is derived from a bottom up approach with targets established for each process. There are two main ways that these targets can be set.

2.6.1.Historic targets

These relate current performance to historic performance with the objective of ensuring that overall improvement does not dip below historic/baseline performance.Easier and quicker to set up than model based targets but can only really identify that you are operating your assets at the same level that you always have done. If undertaken following a site audit then this helps ensure that the site remains operating in the optimised state following the audit. Can also be used as part of an overall strategy of implementing other energy efficiency savings as it delivers the evidence and quantification of energy savings. Can include x% reductions in the target to help establish an incentive to drive down energy use.

2.6.2.Model based

Uses a more process based understanding of the physical processes being used. Derives a process based model that determines the best theoretical performance that the process can deliver.This approach generates more valuable information on the potential for savings but is more complicated to set up since it requires an in-depth understanding of the water and wastewater processes in use at each site to define the model. As per all models this then requires a much more thorough testing routine to validate the model being used.

2.7. Calculation complexity
Calculations can be as complex as required to deliver the relevant KPIs and metrics. MCE supports a broad range of its own functions or more experienced users can use the full range of the C# functions in the Microsoft .NET Framework libraries. This also means that high level uses of MCE can write their own C# functions for complex problems.This level of calculation complexity can also be applied to the targets, high and low alert limits and to the costs. This latter point means that MCE can be used to model any electricity tariff.MCE also retains a dependency tree of calculation in memory allowing chains of calculations to be created. Re-calculation of one will subsequently lead to recalculation of all the other relevant calculation in the tree.

2.8. Automated identification of Savings Opportunities
By aggregating both consumption and target data using a bottom up approach allows the user to create calculations to generate alerts if the sum of the savings (actual use â target use) for the whole Site, or even for particular process, exceeds specified limits which may include a period of time over which the savings should have been occurring and/or a value of savings. This helps to ensure that Opportunities are only identified for meaningful amount of money and are not short duration âblipsâ.

3. Data feeds
All data feeds can be automatically uploaded into MCE as standard semi colon delimited text files in the format Alias; Date and Time; Value.

3.1. SCADA

Main source of process and hours run based data. Look to extract at anywhere from 5-30 minutes periodicity (gap between the raw data readings). Includes analytical sensor based data such as flow, dissolved oxygen, raw water colour/turbidity/ pH etc.

3.2. Laboratory Information Management Systems (LIMS)

Extract historic data once analysed for inclusion in the process based targets and models. Examples of data used include Biological Oxygen Demand (BOD0 mg/l) and ammonia.

3.3. Energy

In the UK this would be the half hour readings from the fiscal settlement meter of from the AMR sub meters.

4. Set up and configuration

4.1. Using Templates

Based on the Companyâs asset taxonomy Meniscus will configure existing process templates to match the naming convention in the taxonomy. This is to ensure that the overall naming convention matches that of the companyâs main asset database.Templates make use of the API capabilities built into MCE â see section 6.4 for more detail â to dramatically reduce the time to set up the database by allowing users to âcopyâ processes with all the calculations from the template into a new Site.

4.2. Configuration with the PC Client

The PC Client application is a thin client application that runs on the customerâs PCâs and is used to configure the entire database. Provides accessibility to every part of the database configuration allowing authorised users the ability to set up their own calculations, targets, costs, conversions and associated high/low alert calculations. Provides the means to download raw and calculated data from the server to the userâs PC from where it can be used in Excel and other applications

4.3. Configuration with the web site
The main MCE web site provides an alternative means to configure the database for simple updates/additions (PC Client is much better for making a number of such changes) but is primarily used for analysis using a range of drill down graphs, dashboards and management reports. The web site is the only way for an authorised administrator to manage access rights and permissions.Designed principally as the management access to the data but is being generally superseded with the range of Silverlight dashboards.

5. Target setting using dashboard
This is a Silverlight dashboard solution aimed at giving the user the ability to select the key data they wish to use to set the targets. Examples are given in Appendix 7.2 and 7.3. This dashboard was developed as a tool to help automatically identify the best pump combination to use for multiple large pump installations.

5.1. Choosing the right time range

Provides small mini trend graphs of each benchmark, KPI, target calculation to help the user select the right range of data around which to set the regression target.

5.2. Setting regressions

Can use a manual method to de-select outliers, or can use an automated calculation methodology, to remove any outliers from the analysis. Regression equation is automatically calculated.

5.3. Applying Targets

Regression equation can be updated into the database as a new target

6. Visualisation

6.1. Dashboard

A range of Silverlight dashboard are available to view a range of management views of the data. These incorporate map based overviews, drill down capability, tables to name a few.

6.2. Reports

A range of over 75 management reports, developed in Business Object â Crystal Reports, are available from the web site. Additional reports can be added/developed as required.

6.3. E-mail alerts

Any calculation or Item in MCE can be set up as an e-mail alert so that an exception e-mail is generated for any instance that the Actual value exceeds the High Limit. By using calculations in the High Limits it is possible to create intelligent alerts to limit the sending out of low value or duplicate alerts.

6.4. Using the Meniscus API

6.4.1.SOAP Web Services

MCE includes a comprehensive set of SOAP web services that are used by the PC Client application. These are complex to use but give full control over every part of the creation, editing and deletion of database entries. These are only used by Developers with a thorough understanding of the MCE system.

6.4.2.Simplified RESTful Web Services

A much simplified set of RESTful web services is available for users that want to, primarily, get data from the Meniscus servers. There is capability to update calculations, targets, costs and high/low limits.

6.4.3.Creating your own dashboards

For users wanting to create their own Silverlight dashboards then Meniscus have a subset of the SOAP web services created for Silverlight. Alternatively the RESTful web services can be used to create dashboards for any other platform or device.

6.4.4 Smartphone/Tablet friendly Widgets

Using the RESTful web services users can create widget style graphs where the output HTML code can be embedded into a web page to create a graph that automatically refreshes with latest data from the server (real time options available).Makes it simple to quickly view trends for any Item you want and to incorporate them into your own applications.An example of the Widget User Interface for selecting the Widget is given in Appendix 7.5.

6.4.5.Creating your own Templates

Using a combination of the SOAP and the RESTful web services allows users to create their own templates for the entire creation of Sites plus all the associated calculations required for that Site. This could allow a user, for example, to register online and this registration activity calls the template to create their Site.

The post Process Based Energy Management for the Water Industry appeared first on Meniscus.

Connecting to the Niagara framework using the Obix framework

scaadmin — Mon, 10 Sep 2012 09:00:15 +0000

We have had the ability to connect to BMS systems running the Niagara Framework for a while now….but not making the most of this technology.

Went to the Energy Event at the NEC today and having spoken with Tridium can see that this is too good an opportunity to miss.

Essentially this already allows us to download data from BMS systems directly into our IPMS database where the Meniscus Calcualtion Engine (MCE) can turn this raw data into any calculated metric that users want. Using our new RESTful web service will allow developers to then extract the calcualted metrics directly from the IPMS database.

So we can developers have a way to extract raw data from the BMS, aggregate it, set targets, apply tariffs, create any metric or benchmark they want ….and then extract this data into their own dashboards….if they want we also have a suite of WCF web services (with a subset of Silverlight Web Services that will allow developers to create their own Silverlight dashboards.

The post Connecting to the Niagara framework using the Obix framework appeared first on Meniscus.