MAP IoT Entities – Introduction

MAP IoT Entities – an Introduction

MAP IoT Entities give you control on how to turn raw data from a sensor, device or …anything, into the analytics you want. Entities include Raw Items and Calc Items and both are contained in an Entity Template. This Entity Template is called as often as you want by importing configuration files. These config files include names and properties of the Entity and the properties for the Raw and Calc Items. On import, the Entity Template creates the Entity and the Raw and Calc Items and immediately starts processing any raw data that is available.

This is the first of several articles that we will write on how to use MAP IoT Entities to deliver your IoT application.

What are MAP IoT Entities?

Raw Items
A Raw Item contains the raw data that you upload into MAP. Raw data contains any Data Type that you want. If we don’t support the Data Type already (we support quite a range) then you can create your own Data Type – article on Data Types.

Calculated Items
A Calc Item contains the metrics that you want to create using your raw data. Rather than create all your analytics in one complex algorithm, our experience is that it it is easier, more flexible and quicker to create a number of seperate Calc Items that each do a specific part of the analytics.

A core module of MAP is the Invalidator. This continually monitors the calculation time of all Items in MAP and dynamically builds a dependency tree of all Items. By defining the type of invalidation relevant to your Calc Item, you control when and how frequently your Items are updated and re-calculated. The default mode is to invalidate on change of latest calculation time. So, if the latest calculation time of a Raw or Calc Item in the Dependency Tree changes then other Calc Items that are dependent on that Item will automatically recalculate.

What this means in practice is:

  • Step 1. New raw data is uploaded into a Raw Item in an Entity
  • Step 2. All Calc Items that depend on the Raw Item or any child Calc Item are also recalculated
  • Step 3. This all happens in seconds

    Why use MAP IoT Entities – what are the benefits?

    The key reason is simplicity. They are easy to use, easy to set up and offer a lot of flexibility.

    MAP is an integrated stack so we do all the complicated plumbing required to deliver the calculated metrics you want. So, all a developer needs to consider is:

  • 1. How to upload raw data from their device to MAP (API call or file drop are the best)
  • 2. How to extract data from MAP for use in their application/dashboard/UI (API call is best)
  • That’s it – MAP takes care of everything else!

    What is MAP?

    MAP stands for the Meniscus Analytics Platform and is MAP is our IOT Analytics Platform for delivering solutions at scale and at speed. It is an Integrated Analytics Stack so you can develop your solutions quicker and easier.

    More information on MAP IoT
    More information on MAP

    New MAP IoT Gateway device

    Our new IoT Gateway device makes it easier for developers to connect to MAP directly from devices.

    The gateway runs as a Windows Service on the IoT device or on a Raspberry Pi or a micro PC. It uses a MAP importer to push and pull data from the device directly into MAP. So, the gateway allows bi-directional flow of data making it possible to send instructions from MAP back to the IoT device.

    Within MAP, making use of our IoT Entity model, developers can create templates containing Items and Properties and add any calculation they want.

    For more information on MAP then click here

    MAP Sewer – creation of simplified sewer network models

    New MAP Sewer capability speeds up the creation of the simplified sewer network models. This makes is quicker and easier to set up our near real time predictive modelling of the sewer network.

    We have been working to speed up the creation of the simplified sewer network models in MAP Sewer so that we can rapidly create new models for new catchments. We have now automated the process of creating the main simplified model, and all the relevant geometries, from the detailed GIS layers that make up the ‘standard’ detailed models used by most water companies.

    The objective of this work is:

  • Generate the MAP Sewer model inputs from the detailed model
  • To do this in an automated way using a combination of QGIS and PYTHON scripts
  • The methodology includes:

  • Derive location of Pumping Stations, Combined Sewer Overflows, Detention tanks, Weirs and Sluices
  • For each Pumping Station, use QGIS flow trace to identify the upstream conduits
  • Identify the sub-catchments associated to these upstream conduits
  • Dissolve the sub-catchments into one large sub-catchment
  • Aggregate the key sub-catchments properties
  • Calculate the main trunk sewer path and aggregate sewer length, gradient and diameter
  • Create the MAP Sewer nodes
  • The process takes several hours to run and the outputs are:

  • MAP Sewer configuration files. These are CSV files for each geometry. I.e. Pumping Stations, Combined Sewer Overflows, Detention tanks, Weirs and Sluices
  • One sub-catchment file containing all the dissolved sub-catchments. this is a KML file
  • Once this is done then we can add some of the pumping attributes to the Pumping Station and Detention Tank geometry files and then load all the files into MAP Sewer from the dashboard. MAP Sewer then creates the geometries in a few minutes and the whole catchment is calculated in 20 minutes – this includes over 2 years of historic data all at 5 minute periodicity. We can now start to validate the model and to feed it with real time and forecast rainfall data.

    Optimize battery storage by predicting power output

    Case study – Short term solar irradiance predictions and impact on PV site revenue

    This case study summarises the work completed in an InnovateUK collaborative research project to optimize battery storage at PV sites using short term solar irradiance predictions. As a result of this work, the project delivered the following outcomes.

  • Deliver solar irradiance predictions for the next 2 1/2 hours at 15 minute intervals using the latest satellite imagery
  • Understand the relationship between solar irradiance and inverter output power
  • Model how short term solar power and solar irradiance predictions can increase revenue from Demand Side Response (DSR) schemes
  • Use this data to optimize battery storage
  • Quantify the financial benefits and the break even point in terms of site of PV site
  • The project uses near real time satellite imagery to predict the path of clouds and to predict the solar irradiance at any location for the next 2 hours at 15 minute increments. Therefore, by predicting the solar irradiance we can predict the solar power output for the site and optimize battery storage and increase revenue from the National Grid’s Demand Side Response programme.

    The Project found that PV sites larger than 2MW would benefit from this technology and it is especially coct effective for sites operating ‘behind the meter’ with either battery storage or with on-site demand.

    The project finished in January 2019 and was built using the Meniscus Analytics Platform (MAP).

    Project partners:

  • Meniscus Systems Ltd: Lead Partner providing the data analytics and processing capability to deliver solar irradiance predictions
  • Open Energi: Providing expertise to deliver accurate, real-time PV-based DSR solutions to DNOs and owner/operators of solar farms
  • BRE National Solar Centre: Responsible for ensuring the system meets the requirements of the PV industry. Providing domain expertise and access/advice on technical solar issues.
  • Cornwall Council: Owner/operator of one of the solar farms used to test and demonstrate the system
  • MAP Rain – New FEH 2013 Rainfall Return Period calculator

    FEH is the industry standard used to estimate local flood risk and develop resilient infrastructure.

    New Service – Rainfall Return Period calculation for any location using the FEH 2013 methodology

    MAP Rain dashboard and rainfall map now includes the updated FEH (Flood Estimation Handbook) 2013 methodology as well as the original FEH99 method. This provides the Return Period calculation for any location and any date in the past 4 years using the MAP Rain dashboard. These Return Period calculations are available for both Points and Polygons.

    You can use the MAP Rain dashboard to calculate:

  • The depth (mm) and duration (hours) of rainfall that generates the largest Return Period on a particular day
  • The depth (mm) of rain for the location that generates a specific Return Period for a specific rainfall duration (hours)
  • For more information on our MAP Rain dashboard and rainfall map click here

    Predicted Rainfall Alerts
    MAP Rain can also apply the FEH 2013 methodology to the forecast rainfall so that we can send you e-mail alerts for any significant rain events that may impact flooding hotspots.

    Return Period calculation API calls

    We have built two API calls into MAP to let you integrate the FEH 2013 return period calculations directly into your own applications. Please note that these call will take about 2 minutes to return.

    Returns depth (mm), duration (hours) and Return Period for a particular day and location


    Date 17th Sept 2017 (rainy day)
    Location in Long Lat (WGS84) or OS Easting and Northing


    Rain Event Start and End Time 14:30 to 16:00
    Duration 1.5 hours
    Max Depth 16.68 mm
    Return Period 1 in 2.45 years

    Returns depth of rain (mm) for a specific duration (hours), return period and location


    Location in Long Lat (WGS84) or OS Easting and Northing
    Duration 5 hours
    Return Period 1 in 20 years

    Max Depth 47.72 mm


    FEH Return Periods calculated by Meniscus through use of FEH1999 and FEH2013 DDF model © and Database right NERC (CEH).

    Stewart, E. J.; Jones, D. A.; Svensson, C.; Morris, D. G.; Dempsey, P.; Dent, J. E.; Collier, C. G.; Anderson, C. A.. 2013 Reservoir Safety – Long Return Period Rainfall. Project FD2613 WS 194/2/39 Technical Report (two volumes). Joint Defra/Environment Agency Flood and Coastal Erosion Risk Management R&D Programme.

    MAP Solar – new service predicts solar power and irradiance at any location

    MAP SOLAR is our new service to predict solar power and irradiance. This is ideal for companies wanting to optimize on-site battery use or improve the management of micro-grids.


    MAP Solar applies Artificial Intelligence and a Block Matching and Relaxation algorithm to the latest satellite imagery to predict the path of clouds. So, for any location in the UK, we can predict solar power and solar irradiance and help you maximise revenue from your solar PV sites.

  • Increase revenue by optimizing on-site battery storage – predict the peaks and troughs in site power use.
  • Combines the latest satellite images with an AI algorithm to predict cloud movement
  • Use the irradiance data to predict power output from your PV installation. The model takes current rainfall into account to improve accuracy between the predicted and actual solar irradiance values.
  • Calculates cloud cover and applies this to a Clear Sky solar irradiance model to calculate diffuse, direct and combined in-plane solar irradiance.
  • Satellite images are updated every 15 minutes and we predict solar irradiance for the next two hours at 15 minute intervals
  • Get solar irradiance predictions for any location in the UK. Available from dashboard or our API
  • Actual and Predicted power (kW) compared with actual irradiance data (W/m2)

    For more information then view our MAP Solar solution page or Send us a message or give us a call on 01480 433714.


    This was funded under an InnovateUK Collaborative Project. Our partners are:

    Lead Partner providing the data analytics and processing capability to deliver solar intensity predictions. All predictive analytics are delivered using the Meniscus Analytics Platform (MAP).

    Energy tech partner providing expertise to deliver accurate, real-time PV-based Demand Side Response solutions to Distribution Network Operators and owner/operators of solar farms to more efficiently manage local networks and generate income.

    BRE – National Solar Centre is responsible for ensuring the system meets the requirements of the PV industry and providing domain expertise and access/advice on technical solar issues.

    Owner of one of the solar farms used to test and demonstrate the system.

    MAP Rain – rainfall map and analytics for urban areas

    We are pleased to announce the introduction of a new geometry in MAP RAIN that delivers big cost reductions. This is ideal for large rural agencies who want a rainfall map and rainfall analytics data for their urban areas.

    A new Multi-Polygon geometry delivers a rainfall map for just the areas that area of specific interest to you. Before this, we had to provide rainfall and associated data for the whole area of interest.

    Click here for more information on MAP Rain and rainfall map

  • Example: A Lead Flood Authority with a large predominately rural area of say 10,000km2 only wants real time and predictive rainfall analytics and access to FEH data for the urban areas, say 750km2. Previously, we had to provide rainfall data for the whole 10,000 km2 area and then add Polygons within this for specific catchments of interest. With the new Multi-Polygon geometry we can provide the customer with these analytics for JUST the urban areas. This delivers a big reduction in the cost of accessing rainfall analytics information from MAP Rain. I.e.MAP Rain prices are based on 750km2 rather than 10,000km2.
  • Example of multi-polygon area

    To receive a quote for using MAP Rain in your are then please send us a message from the Contact Page

    MAP Rain – updated imagery for rainfall map

    We recently updated MAP Rain to display rainfall as an image making it much faster to display new images. Previously we displayed rainfall for each individual 1 km square cell. MAP Rain processes data in km squares using the Ordnance Survey Grid Reference system but the dashboard uses the WGS84 projection. So to produce a suitable image we have to go through several stages.

  • Use the four corners of the visible area of the map and return the min/max Easting and Northings required to fully display the image. We add a small amount to each side to ensure it is covered on the screen.
  • Render an image for these Easting and Northings values from the internal grid that represents the data at the relevant time.
  • Then ‘warp’ this image to change the projection from a flat grid reference to the representation of that grid on the map. This is why the top and bottom of the returned trapezoid are curved and it is wider at the top than the bottom (imagine taking a sheet of paper and placing on a globe). We then display this image on the dashboard.
  • This process allows us to return different ‘zoom’ levels of the image with each having a better resolution. Most other mapping solutions limit the zoom level as they only display the one image for the whole of the UK.

    Click here for more information on MAP Rain and our rainfall map and dashboard

    Areal FEH 99 Return Period calculation for polygons

    As part of our aim of continuing to add complex rainfall analytics into MAP Rain we have added the Areal FEH 99 return period calculation that lets you create the areal return period for a polygon – this is the methodology as set out in the Flood Estimation Handbook.

    How to run

  • From the dashboard click on Rainfall Return Period
  • Set the date you are interested in using the top date selector
  • Select the polygon you are interested in
  • Select the “Get Return Period for Item” option under Rainfall Return Period
  • MAP Rain will then calculate the Areal FEH 99 Return Period and display all the coefficients and results in the map results pane along with a graph of the rainfall intensity for 3 days – includes the previous and the next days.

    Click here for more information on MAP Rain

    Yorkshire Water network resilience hackathon

    Overview of a one day hackathon using a closed data set to investigate how data analytics can help find the best locations for some +8,000 sensors. Details the solution delivered and developed on the day