Forecasting and Energy Information Services

Transcription

1 Forecasting and Energy Information Services Henrik Aalborg Nielsen A/S Henrik Aalborg Nielsen, A/S 1

2 Outline Cloud service solution. Consumption and production forecasts. Monitoring signals for systematic changes. Smart meter readings. Henrik Aalborg Nielsen, A/S 2

3 Energy forecast solutions Cloud service solution FTP / SFTP file up- and down-loads with retries and book-keeping. Client-specific (SOAP, XML, ) Highly configurable HTTPS based graphical user interface, supporting access level restrictions. Security of supply (NWP providers, data centres, ) Data security (firewalls, encryption, ) Henrik Aalborg Nielsen, A/S 3

4 Consumption and production forecasts Heat load forecasts and temperature optimization for district heating systems usually for a horizons up to one week. Power load forecasts for horizons up to one week, could be longer. Wind power production forecasts usually day ahead, but up to one week is feasible. Solar PV power production forecasts usually day ahead, but up to one week is feasible. Henrik Aalborg Nielsen, A/S 4

5 General forecast principle (Multiple) Numerical Weather Forecast Forecast Engine Calibrated against actual production / consumption available on-line or off-line in batches. If on-line data is available the autocorrelation is used in order to improve short-term performance. Henrik Aalborg Nielsen, A/S 5 Forecast Value(s) Actual Value System

6 Example: SolarFor combined phys./stat. model Henrik Aalborg Nielsen, A/S 8 Core of model based on physical principles (direct/diffuse irradiation, panel efficiency, ). Model characteristics estimated from NWP data and actual PV power production. An number of secondary NWP variables (e.g. atmospheric water content) used in order to detect systematic NWP errors via data mining / machine learning methods. Continuous re-calibration of models.

7 Comparison of models using ECMWF 12z Henrik Aalborg Nielsen, A/S 10

8 SolarFor using GFS-HD 18z / ECMWF 12z Henrik Aalborg Nielsen, A/S 11

9 Automatic monitoring signals for changes Issue alarms if signal properties (e.g. mean and variance) change systematically. Setup by specifying how fast a given change should be detected, together with an acceptable false alarm rate. Monitor for fixed level or tracks the level. Henrik Aalborg Nielsen, A/S 12

10 Example of monitored signals Wind farm / turbine performance indices (with DONG Energy). Household electricity standby power consumption (previously with Elsparefonden). Monitoring of data quality, e.g. the fraction of missing, out of range, and frozen values. Monitoring of excess energy consumption not explainable via climate measurements. Henrik Aalborg Nielsen, A/S 13

11 Use of smart meter readings (frequent energy measurements from houses / households, e.g. hourly values) Cost-efficient district heating temperature optimization using closed-loop control (on-line measurements). Screening for houses / households with poor energy performance (off-line measurements). Henrik Aalborg Nielsen, A/S 14

12 PRESS-TO for DH systems Temperature Optimisation. Keep the network temperature as low as possible, while meeting consumer energy and temperature demands. Closed-loop control using on-line measurements of network temperature. New development?: Use smart meter measurements of building supply temperature as a replacement for network measurements. Henrik Aalborg Nielsen, A/S 15

13 Målinger Screening - koncept Estimation for individuelle huse / husholdninger Forbruger (hus) N: Forbruger (hus) 2: Forbruger (hus) 1: Følsomhed for udetemp., sol, vind. Varmtvandsforbrug. Fyrringssæsson. Baggrundsdata, hus N: Baggrundsdata, hus 2: Baggrundsdata, hus 1: Opvarmningstype Grundareal Sammenligning på tværs af huse / husholdninger Liste over huse / husholdninger med en usædvanlig høj klimafølsomhed, højt varmtvandsforbrug, Henrik Aalborg Nielsen, A/S 16

14 Thank you for your attention! Henrik Aalborg Nielsen, A/S 17