Forecasting and Energy information
Transcription
Forecasting and Energy information
CITIES 2nd General Consortium Meeting 25-26 May, 2015, DTU, Lyngby ENFOR Forecasting and Energy Information Services Henrik Aalborg Nielsen ENFOR A/S [email protected] Henrik Aalborg Nielsen, ENFOR A/S 1 CITIES 2nd General Consortium Meeting 25-26 May, 2015, DTU, Lyngby ENFOR Outline • Cloud service solution. • Consumption and production forecasts. • Monitoring signals for systematic changes. • Smart meter readings. Henrik Aalborg Nielsen, ENFOR A/S 2 ENFOR 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, ENFOR A/S 3 CITIES 2nd General Consortium Meeting 25-26 May, 2015, DTU, Lyngby ENFOR 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, ENFOR A/S 4 CITIES 2nd General Consortium Meeting 25-26 May, 2015, DTU, Lyngby ENFOR General forecast principle (Multiple) Numerical Weather Forecast Forecast Engine Forecast Value(s) Actual Value System • 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, ENFOR A/S 5 CITIES 2nd General Consortium Meeting 25-26 May, 2015, DTU, Lyngby ENFOR Example: SolarFor – combined phys./stat. model • 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. Henrik Aalborg Nielsen, ENFOR A/S 8 CITIES 2nd General Consortium Meeting 25-26 May, 2015, DTU, Lyngby ENFOR Comparison of models using ECMWF 12z Henrik Aalborg Nielsen, ENFOR A/S 10 CITIES 2nd General Consortium Meeting 25-26 May, 2015, DTU, Lyngby ENFOR SolarFor using GFS-HD 18z / ECMWF 12z Henrik Aalborg Nielsen, ENFOR A/S 11 CITIES 2nd General Consortium Meeting 25-26 May, 2015, DTU, Lyngby ENFOR 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, ENFOR A/S 12 CITIES 2nd General Consortium Meeting 25-26 May, 2015, DTU, Lyngby ENFOR 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, ENFOR A/S 13 CITIES 2nd General Consortium Meeting 25-26 May, 2015, DTU, Lyngby ENFOR 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, ENFOR A/S 14 CITIES 2nd General Consortium Meeting 25-26 May, 2015, DTU, Lyngby ENFOR 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, ENFOR A/S 15 CITIES 2nd General Consortium Meeting 25-26 May, 2015, DTU, Lyngby Målinger ENFOR Screening - koncept Estimation for individuelle huse / husholdninger Baggrundsdata, hus N: … Baggrundsdata, hus 2: … Baggrundsdata, hus 1: • Opvarmningstype • Grundareal • … Forbruger (hus) N: … Forbruger (hus) 2: … Forbruger (hus) 1: • • • Følsomhed for udetemp., sol, vind. Varmtvandsforbrug. Fyrringssæsson. Sammenligning på tværs af huse / husholdninger Liste over huse / husholdninger med en usædvanlig høj klimafølsomhed, højt varmtvandsforbrug, … Henrik Aalborg Nielsen, ENFOR A/S 16 CITIES 2nd General Consortium Meeting 25-26 May, 2015, DTU, Lyngby Thank you for your attention! Henrik Aalborg Nielsen, ENFOR A/S 17 ENFOR