Artificial Neural Network for Short-Term Load Forecasting in Distribution Systems

Luis Hernández,Javier Aguiar,Belén Carro,Ángel Fernández,Jaime Lloret,Antonio Sánchez-Esguevillas,Lorena Calavia,Carlos Baladrón,Francisco Pérez

doi:10.3390/en7031576

Abstract

The new paradigms and latest developments in the Electrical Grid are based on the introduction of distributed intelligence at several stages of its physical layer, giving birth to concepts such as Smart Grids, Virtual Power Plants, microgrids, Smart Buildings and Smart Environments. Distributed Generation (DG) is a philosophy in which energy is no longer produced exclusively in huge centralized plants, but also in smaller premises which take advantage of local conditions in order to minimize transmission losses and optimize production and consumption. This represents a new opportunity for renewable energy, because small elements such as solar panels and wind turbines are expected to be scattered along the grid, feeding local installations or selling energy to the grid depending on their local generation/consumption conditions. The introduction of these highly dynamic elements will lead to a substantial change in the curves of demanded energy. The aim of this paper is to apply Short-Term Load Forecasting (STLF) in microgrid environments with curves and similar behaviours, using two different data sets: the first one packing electricity consumption information during four years and six months in a microgrid along with calendar data, while the second one will be just four months of the previous parameters along with the solar radiation from the site. For the first set of data different STLF models will be discussed, studying the effect of each variable, in order to identify the best one. That model will be employed with the second set of data, in order to make a comparison with a new model that takes into account the solar radiation, since the photovoltaic installations of the microgrid will cause the power demand to fluctuate depending on the solar radiation.

Highlights

Since the origins of the electrical system, engineers have always tried to understand its operation, which was relatively simple but has become so difficult to manage and control in our days
For all the models the architecture is based on Multi-Layer Perceptrons (MLP), which will require a learning function on a set of patterns, and subsequently will require validation in a phase of operation with another set of data not used in the learning
Forecasting is more complex in a microgrid due to the increased variability of disaggregated load curves

Summary

Introduction

Since the origins of the electrical system, engineers have always tried to understand its operation, which was relatively simple but has become so difficult to manage and control in our days. Spencer and Hazen [1] present in 1925 an artificial system to simulate and study Kirchhoff’s laws with different loads, and with alternating and direct current. Hamilton [2] makes a study of the addition of curves for characterization of the load curve on the basis of a set of meaningful features: peak load, minimum load and load factor; posing the prediction of the load as a challenge for the operation. Forrest [3] presents the statistical treatment of historical data, the study of the load fluctuation with the climatic conditions and its forecasting, as fundamental to avoid breakage of electrical installations because of climatic anomalies. Gruetter [5] uses punch cards from public administration for the prediction of the load, based on statistical averages of consumption behaviours

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Conclusion