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Projects: Projects for Investigator
Reference Number NIA_NGSO0015
Title Optimisation of weather data to improve energy forecasting
Status Completed
Energy Categories Other Power and Storage Technologies(Electricity transmission and distribution) 20%;
Other Cross-Cutting Technologies or Research(Environmental, social and economic impacts) 80%;
Research Types Applied Research and Development 100%
Science and Technology Fields ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 20%;
ENVIRONMENTAL SCIENCES (Earth Systems and Environmental Sciences) 80%;
UKERC Cross Cutting Characterisation Not Cross-cutting 100%
Principal Investigator Project Contact
No email address given
National Grid plc
Award Type Network Innovation Allowance
Funding Source Ofgem
Start Date 01 May 2018
End Date 01 November 2019
Duration ENA months
Total Grant Value £124,000
Industrial Sectors Power
Region London
Programme Network Innovation Allowance
Investigators Principal Investigator Project Contact , National Grid plc (100.000%)
  Industrial Collaborator Project Contact , National Grid plc (0.000%)
Web Site https://smarter.energynetworks.org/projects/NIA_NGSO0015
Objectives This research project seeks to understand the extent to which increasing the frequency and spatial resolution of forecasts in the weather data used can improve the accuracy of demand, PV and wind generation forecasts. The proposed work and methodology will also aim to make recommendations on the weather variables which should be used in PV, wind and demand forecasting models to deliver an optimum result. This will be achieved through analysing historical weather data as part of the project and answering the following research questions: What is the optimal frequency and resolution for the GB System Operator to receive weather forecasts? What is the optimal number and locations of weather stations? Which weather variables (e.g. temperature, wind speed) are best to use for energy forecasting? What are the impacts of weather forecast error on the forecast accuracy of PV generation, wind generation and national demand? To answer 1) above, the project will use the data from the historical weather forecasts created four times a day and compare them against the actual values to understand the relationship between lead-in time and errors in weather forecasts. Through this approach, it is expected that a functional relationship can be created to determine the weather forecast sensitivity to lead-in time, and thus answer questions such as “what is the expected weather forecast error if the lead-in time is 1 hour?”. In answering this question, the impact of weather data on energy demand forecasting can be better understood,The analysis and methodology for answering question 2) above will be based on data from weather stations where both forecast data and actual observed data are available. The output of this analysis will be graphs displaying how errors in weather forecasts (i.e. temperature, wind speed and radiation) depend on the number of weather stations and the average distance to the nearest neighbouring weather station. These investigations will help understanding how spatial representation of weather data impacts solar PV, wind and overall energy demand forecasting. To answer question 3, a thorough review will be conducted to determine the state of the art of wind, PV and demand forecasts models and what weather variables they incorporate. These activities will inform the future energy forecasting strategy. Investigations for answering question 4) will specifically consider how errors in weather forecasts and the associated data (from the learnings and analysis carried out in answering questions 1, 2 and 3) propagate through the forecasting models and give rise to errors in MW outturn in the overall energy demand forecast. This innovation project involves analysis of vast sets of historical weather data to investigate the effects of increasing the frequency of delivery of weather forecasts, the spatial resolution of weather forecasts and assess which weather variables National Grid should use to optimally estimate demand, wind and PV generation. The objective of this project is to identify the properties of weather data which will optimise generation and energy demand forecast accuracy in the most cost-effective way.
Publications (none)
Final Report (none)
Added to Database 02/12/22