Abstract
Load forecasting plays a major role in determining the prices of the energy supplied to end customers. An accurate prediction is vital for the energy companies, especially when it comes to the baseline calculations that are used to predict the energy load. In this paper, an accurate short-term prediction using the Exponentially Weighted Extended Recursive Least Square (EWE-RLS) algorithm based upon a standard Kalman filter is implemented to predict the energy load for blocks of buildings in a large-scale for four different European pilot sites. A new software tool, namely Local Energy Manager (LEM), is developed to implement the RLS algorithm and predict the forecast for energy demand a day ahead with a regular meter frequency of a quarter of an hour. The EWE-RLS algorithm is used to develop the LEM in demand response for blocks of buildings (DR-BOB), this is part of a large-scale H2020 EU project with the aim to generate the energy baselines during and after running demand response (DR) events. This is achieved in order to evaluate and measure the energy reduction as compared with historical data to demonstrate the environmental and economic benefits of DR. The energy baselines are generated based on different market scenarios, different temperature, and energy meter files with three different levels of asset, building, and a whole pilot site level. The prediction results obtained from the Mean Absolute Percentage Error (MAPE) offer a 5.1% high degree of accuracy and stability at a UK pilot site level compared to the asset and whole building scenarios, where it shows a very acceptable prediction accuracy of 10.7% and 19.6% respectively.
Highlights
An energy baseline profile is a reference which allows a comparison between the energy performance before and after a change made to the site or system in a demand response (DR)situation
We present mathematical models and software solution for short-term load prediction, which uses an Exponentially Weighted Extended Recursive Least Square (EWE-RLS) algorithm in a similar approach to the work of Short [17]
The baseline has been generated for all of these levels, but three meters located at different levels with a Meter Point Administration Number (MPAN) and respective meter names enclosed in bracket have been selected as follows: 1332152031 (Main site electrical meter), 1332152080 (Clarendon electric sub-meter), and 1332152041 (Phoenix main electric meter)
Summary
An energy baseline profile is a reference which allows a comparison between the energy performance before and after a change made to the site or system in a demand response (DR). To implement the prediction model as a component of Local Energy Manager (LEM) deployed in DR-BoB pilot demonstration sites to provide heat and electricity demand forecasts, in addition to LEM’s optimization function, which is neglected in this paper. Thesites, baseline resultsthis of this work have been evaluated at LEM interfaces at each of the as is part of the integration of the different stages to measure the accuracy of the load forecasting prediction a day ahead, assessed by equipment itself, it is outside the scope of this study. One of the first aims of developing and using LEM is to manage, generate and identify the Demand Response Technology Readiness Level for each site and the necessary modifications required to enable the DR-BOB LEM to generate the baseline for different energy meters and temperature files. The OpenVPN connection is established first, communication using the OpenADR 2.0 protocol can proceed
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