Adequacy of neural networks for wide-scale day-ahead load forecasts on buildings and distribution systems using smart meter data

Oleg Valgaev,Hartmut Schmeck,Friederich Kupzog

doi:10.1186/s42162-020-00132-6

Oleg Valgaev, Hartmut Schmeck + Show 1 more

Open Access

https://doi.org/10.1186/s42162-020-00132-6

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Abstract

Power system operation increasingly relies on numerous day-ahead forecasts of local, disaggregated loads such as single buildings, microgrids and small distribution system areas. Various data-driven models can be effective predicting specific time series one-step-ahead. The aim of this work is to investigate the adequacy of neural network methodology for predicting the entire load curve day-ahead and evaluate its performance for a wide-scale application on local loads. To do so, we adopt networks from other short-term load forecasting problems for the multi-step prediction. We evaluate various feed-forward and recurrent neural network architectures drawing statistically relevant conclusions on a large sample of residential buildings. Our results suggest that neural network methodology might be ill-chosen when we predict numerous loads of different characteristics while manual setup is not possible. This article urges to consider other techniques that aim to substitute standardized load profiles using wide-scale smart meters data.

Highlights

Following the ongoing transformation of the European power system, it will be necessary to locally balance the increasing share of decentralized renewable energy supply
artificial neural networks (ANN) have been applied for predicting day-ahead loads of larger buildings (Bagnasco et al 2015; Chitsaz et al 2015a; Ryu et al 2016), consumer aggregations (Marinescu et al 2013) and microgrids (Amjady et al 2010; Hernández et al 2014)
Our results suggest that ANN methodology might be inapt for wide-scale local load forecasting

Summary

Introduction

Following the ongoing transformation of the European power system, it will be necessary to locally balance the increasing share of decentralized renewable energy supply. Distribution system operation will require a versatile and reliable model to obtain numerous day-ahead load forecasts (DALF) on various levels of load aggregation. ANNs have been applied for predicting day-ahead loads of larger buildings (Bagnasco et al 2015; Chitsaz et al 2015a; Ryu et al 2016), consumer aggregations (Marinescu et al 2013) and microgrids (Amjady et al 2010; Hernández et al 2014). Local loads Local loads range from single family homes to a small distribution system area supplied by an MV/LV substation including microgrids (Dang-Ha et al 2017). Their consumption is very diverse and more volatile than the transmission system load. Load is autoregressive and underlies annual and weekly seasonalities but to different extent depending on the size (Sevlian and Rajagopal 2014) (Fig. 1)

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