4th International Conference on Renewable Energy Research and Applications Palermo, Italy, 22-25 Nov 2015 Novel Energy Modelling and Forecasting Tools for Smart Energy Networks G. Sauba*,Jos van der Burgt*, A. Schoofs**, C. Spataro***, M. Caruso***, F. Viola*** and R. Miceli*** *SR&I, Power & Electrification DNV GL Arnhem, The Netherlands **Wattics Ltd. Dublin, Ireland ***Department of Energy, Information engineering and Mathematical models (DEIM) University of Palermo Palermo, Italy [email protected] Abstract—A novel Energy Modelling and Forecasting Tool data generated for the time period required. This can range (EMFT) has been adopted for use in the VIMSEN (Virtual from intra-day to day-ahead in most normal operations but it Microgrids for Smart Energy Networks) project and this paper can extend to week-ahead, month-ahead or even year-ahead. gives an insight of the techniques used to provide vital support to The latter is mostly used for planning purposes and it is very the energy market, in particular to energy aggregators. A brief dependent on quality of the data feed to get the desired description of one of the test sites where data has been collected accuracy expected. for validation of the EMFT will be outlined and some examples shown. The information and predictions will then be used by a For the VIMSEN project the EMFT will make predictions decision support system to dynamically adjust energy delivery on the future energy consumption and generation patterns and consumption, by giving advice to users and operators on either within a single micro-grid or over the collection of the actions they can take to obtain a better match between energy distributed micro-grids. This information and predictions will supply and demand that increases the fraction of energy then be used by the decision support system to dynamically generated by environmentally friendly sources. The Energy adjust energy delivery and consumption, by giving advice to Modelling part of the tool provides input data to the forecasting users and operators on actions they can take to obtain a better section which in turn uses a range of mathematical engines can match between energy supply and demand that increases the analyse the data inputs and generate appropriate forecasting fraction of energy generated by environmentally friendly data for the time period required. This can range from intra-day sources. The Energy Model is a generic tool to estimate to day-ahead in most normal operations but it can extend to consumption for a specific building type based on input data week-ahead, month-ahead or even year-ahead. This is an held within a Microsoft Access database. This flexibility ongoing project of 36 months duration with a consortium of 8 members from the EU and we are half way through the work allows the Energy Model to be run under different conditions being assigned. to carry out ‘what if?’ scenarios and assesses the impact of changes on demand. Keywords—Energy Modelling, Forecasting, Smart grids, energy managment. II. THE ENERGY MODELLING COMPONENT Figure 1 is a schematic view of the generic Energy Model I. INTRODUCTION showing the types of input data used and the output results The Energy Modelling and Forecasting Toolkit (EMFT) is produced. Simulations are performed at building type level. part of the VIMSEN Information Management and Decision ENVIRONMENT • Temperature BUILDING Making Framework. The energy modelling part of the toolkit CONSUMPTION PROFILE • Solar Flux assesses the characteristics of a building with its relevant • Size 700 • Heat Loss 600 500 properties and contents both in terms of appliances and • Glazing 400 300 occupancy; then it calculates the energy used and produced 200 100 0 over a certain period of time as required. Other parameters 00:00 04:00 08:00 12:00 16:00 20:00 APPLIANCES • Gas such as seasonal variation and local weather can be •Power Consumption • Electricity •On • Generation incorporated to reflect a more accurate output. The outputs can •Standby • Mean/SD •Efficiency be aggregated for a period of time and the required profiles •Programme/Cycle generated for the building in question; only one dwelling (domestic or small commercial) can be treated at any USERS • Number particular time. The output from the energy modelling section • Activity Profile of the toolkit will then feed into the input stream of the •Energy Efficiency Attitude forecasting section. A range of mathematical engines can then Fig. 1. Schematic of energy model data and results. be used to analyse the data inputs and appropriate forecasting ICRERA 2015 4th International Conference on Renewable Energy Research and Applications Palermo, Italy, 22-25 Nov 2015 Each building type is characterised by a number of III. THE FORECASTING COMPONENT properties which are defined by the user and stored within the Forecaster is a product developed by DNVGL, built on database e.g. floor area, window area, wall U-value etc. The over 30 years’ experience in the forecasting arena, it is number of different building types required will depend on the designed to improve the process of making trading and amount of variation in the building stock of the network to be balancing decisions by providing accurate energy demand studied. Each simulation calculates the building heat loss and forecasts. appliance consumptions on a minute by minute basis for a day. Usually, a large number of simulations (days) are run for each It is a robust business application built to provide a wide building and averaged. A number of different building range of demand forecasts. Forecaster provides the accuracy instances can also be generated. These are all of the same of results, speed of processing and the business construction type (similar heat loss, glazing area etc.), but will communication and publishing to meet and support the needs have different appliances randomly generated based on of today and the evolving energy markets. specified probabilities. The building’s occupants (users) are Forecaster has been designed to be applicable to many simulated on a probabilistic basis around typical activities at players in the energy industry from transporters and different times of the day. The appliance models then simulate distributors to shippers. This has resulted in a product that is the energy consumption/generation behaviour of each easily configured to meet each client’s specific needs. appliance in response to demand inputs (from users or controls) and, where relevant, to internal building Its flexibility provides: temperatures or the state of other related appliances. Performing multiple simulations for each building type Generic forecasting model methods including: provides diversity in the resulting consumption profiles. Each o Neural Networks appliance, building and user model has a range of parameters that can be changed such as building heat loss, heating o Multiple linear and non-linear demand periods, appliance efficiencies, heating set regression temperatures etc. in order to implement the differences at each o ARIMA (Auto-Regressive Integrated stage of the scenario. Moving Average) Simulations can be run for an ‘average day’ each month. o Bayes For each month, multiple building instances are used, with each building having a different population of appliances. One o Profiler or more simulations are done on each building instance with o Adaptive Combination, several model user behaviour, temperature and solar parameters varying results are combined into one forecast between each run. The result is an average consumption weighted based on recent performance profile (mean and standard deviation) for each month, along with an estimate of the annual consumption (AQ), calculated Flexible scheduling of all forecasting activities by summing the monthly consumption weighted by the number of days in the month. This approach is useful for Configurable forecast horizon for short (hourly, testing, as it generates an estimate of AQ which can be daily) or medium (out to a year) or long term compared to known values. The monthly profiles are also (beyond a year) forecasts useful for assessing the changes in consumption behaviour at Hourly profile forecasts and daily total forecasts different times of the year. Example electricity consumption profiles for a typical January and August are shown in Figure Flexible definition of model input variables and 2. input data streams without changing the software A powerful model configuration utility to allow modellers to select model parameters, model Example Electricity Consumption Profiles by Month for 3 Bed specific settings, training data sets and data Terraced House intervals 120 100 The ability to create individual and/or aggregate client forecasts at customised portfolio or 80 Jan operational or geographic levels 60 Aug 40 A combined forecast to automatically choose the best result (using the adaptive combination lectricity Consumptionlectricity (Wh) 20 E model) 0 :00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 00 The ability to account for special events such as Time national holidays and known shutdowns Fig. 2. Example of energy model consumption profiles. A variety of graphs and performance metrics showing forecast errors and performance against actual consumptions ICRERA 2015 4th International Conference on Renewable Energy Research and Applications Palermo, Italy, 22-25 Nov 2015 Facility to export information to Excel or The Forecaster database also includes a set of database Comma Separated Value files views that simplify the extraction of information from the database. This is known as the “Reporting Layer”, clients can A comprehensive reporting layer to allow more use their favourite reporting tools to view all of the detailed access to the data stored in Forecaster information in the Forecaster Database. Typically clients will e.g. web-based access or other analysis tools via use the Reporting Layer to source key forecast result ODBC information for display in existing business systems, Bulk model assignment, training, activation and dashboardsdashboards or control room displays.