Pauli Murto (1998), Neural network models for short-term load forecasting

HELSINKI UNIVERSITY OF TECHNOLOGY Department of Engineering Physics and Mathematics NEURAL NETWORK MODELS FOR SHORT-TERM LOAD FORECASTING Pauli Murto Supervisor: Professor Raimo P Hämäläinen Instructor: Lic Tech Arto Juusela Helsinki, January 5, 1998 HELSINKI UNIVERSITY OF TECHNOLOGY ABSTRACT OF MASTER'S THESIS Author: Pauli Murto Title of thesis: Neural network models for short-term load forecasting Date: January 5, 1998 Pages: 92 Department: Department of Engineering Physics and Mathematics Chair: Supervisor: Instructor: Professor Raimo P Hämäläinen Lic.Tech Arto Juusela, ABB Power Oy Abstract: Neural network techniques have been recently suggested for short-term load Mat-2 Applied Mathematics forecasting by a large number of researchers This work studies the applicability of this kind of models The work is intended to be a basis for a real forecasting application First, a literature survey was conducted on the subject Most of the reported models are based on the so-called Multi-Layer Perceptron (MLP) network There are numerous model suggestions, but the large variation and lack of comparisons make it difficult to directly apply proposed methods It was concluded that a comparative study of different model types seems necessary Several models were developed and tested on the real load data of a Finnish electric utility Most of them use a MLP network to identify the assumed relation between the future load and the earlier load- and temperature data The models were divided into two classes First, forecasting the load for a whole day at once was studied Then hourly models, which are able to update the forecasts as new data arrives, were considered The test results showed, that the hourly models are more suitable for a forecasting application The forecasting errors were smaller than with a SARIMAX model, which was tested for the comparative purpose The work suggests that this kind of an hourly neural network model should be implemented for a thorough on-line testing in order to get a final opinion on its applicability Key words: Not borrowable till: Short-Term Load Forecasting (STLF), Neural Networks, Multi-Layer Perceptron (MLP) networks Library code: (filled by the secretary) TEKNILLINEN KORKEAKOULU DIPLOMITN TIIVISTELMÄ Tekijä: Pauli Murto Tn nimi: Sähkưnkulutuksen lyhyen aikavälin ennustaminen neuroverkkomalleilla English title: Neural network models for short-term load forecasting Päivämäärä: 1998 Sivumäärä: 92 Osasto: Teknillisen fysiikan ja matematiikan osasto Professuuri: Mat-2 Sovellettu matematiikka Työn valvoja: Työn ohjaaja: Professori Raimo P Hämäläinen Tekn.lis Arto Juusela, ABB Power Oy Tiivistelmä: Viime aikoina useat tutkijat ovat ehdottaneet neuroverkkoihin perustuvia malleja sähkönkulutuksen lyhyen aikavälin ennustamiseen Tässä työssä tutkitaan tällaisten mallejen soveltuvuutta Työ on tarkoitettu perustaksi varsinaiselle ennustesovellukselle Työssä perehdyttiin ensin aiheeseen liittyvään kirjallisuuteen Useimmat kirjallisuudessa esitetyistä menetelmistä perustuvat niin kutsuttuun monikerrosperceptron (Multi-Layer Perceptron, MLP) verkkoon Vaikka ehdotettuja malleja on paljon, niiden vaihtelevuus ja keskinäisen vertailun puuttuminen tekevät menetelmien suoran soveltamisen vaikeaksi Pääteltiin, että on välttämätöntä vertailla kokeellisesti erilaisia mallityyppejä Työssä kehitettiin erityyppisiä malleja, joita tutkittiin erään suomalaisen sähkölaitoksen todellisella kulutusaineistolla Useimmat mallit perustuvat monikerrosperceptron verkkoon Tällä pyrittiin mallintamaan tulevien kulutusarvojen oletettu riippuvuus aiemmista kulutus- ja lämpötila-arvoista Mallit jaettiin kahteen luokkaan Ensin tutkittiin koko vuorokauden kulutuskäyrän ennustamista yhdellä kertaa Sitten siirryttiin tarkastelemaan tunneittaisia malleja, joilla ennustetta voidaan päivittää aina kun saadaan uutta tietoa Koetulokset näyttivät, että tunneittaiset mallit ovat sopivampia ennustesovellusta ajatelleen Ennustusvirheet olivat pienempiä kuin SARIMAX mallilla, jota tarkasteltiin vertailukohteena Työssä ehdotetaan, että tälläinen tunneittainen neuroverkkomalli tulisi ottaa perusteelliseen koekäyttöön, jotta saataisiin lopullinen käsitys sen soveltuvuudesta Avainsanat: Ei lainata ennen: Sähkönkulutuksen ennustaminen, neuroverkot, monikerros-perceptron verkko Työn sijaintipaikka: (osastosihteeri täyttää) Preface I have prepared this thesis at Control Systems unit of ABB Power Oy I want to thank the head of the unit, Aimo Sorsa, for providing the opportunity to the work I would also like to thank my instructor Arto Juusela and supervisor Raimo P Hämäläinen Juha Toivari deserves my thanks for many practical hints Particularly, I want to thank Tuomas Raivio for critical comments and valuable advice regarding this work Pauli Murto Helsinki, January 5, 1998 CONTENTS INTRODUCTION 1.1 BACKGROUND 1.2 PURPOSE OF THE WORK 1.3 STRUCTURE OF THE WORK LOAD FORECASTING 10 2.1 FACTORS AFFECTING THE LOAD 10 2.2 PROPERTIES OF THE LOAD CURVE 11 2.3 POSSIBLE APPROACHES 15 Classifications of methods 15 Some of the most popular methods 16 Time-of-day models 16 Regression models 17 Stochastic time series models .17 State-space models 19 Expert systems 20 NEURAL NETWORKS IN LOAD FORECASTING 21 3.1 MULTI-L AYER PERCEPTRON NETWORK (MLP) 22 Description of the network 22 Learning 24 Generalization 25 3.2 MLP NETWORKS IN LOAD FORECASTING 26 3.3 LITERATURE SURVEY 28 Basic MLP-models 28 Peak, valley, and total load forecasting 29 Hourly forecasting 30 Unsupervised learning models 31 Other reported approaches 33 Summary 35 FORECASTING THE DAILY LOAD PROFILE 37 4.1 FORECASTING WITH PEAK, VALLEY, AND AVERAGE LOADS 37 Description of the MLP network models 37 Predicting the shape of the load curve 40 Classifying the days 40 The load shape based on peak- and valley loads 41 The load shape based on average load .41 Different averaging models 42 Test results 42 Error measure 42 Peak, valley, and average load forecasts 43 Load shape forecasts 48 Combining peak, valley, and average load forecasts with load shape predictions 52 4.2 FORECASTING DAILY LOAD ON THE BASIS OF THE PREVIOUS DAY 54 Basic idea 54 Using Kohonen's self organizing feature map 55 Overview of the model .55 Test results 56 A simple selection model 58 4.3 SUMMARY 60 MODELS FOR HOURLY FORECASTING 61 5.1 CHOOSING THE STRUCTURE OF THE MODELS 61 5.2 THE MODEL FORECASTING HOUR BY HOUR 63 Description of the model 63 Results in forecasting one day at a time 65 Forecasting without temperature data 65 Including the temperature 70 The average errors for different lead times 74 Results in forecasting for one week at once 76 5.3 COMPARISON TO A SARIMAX MODEL 77 5.4 UTILIZING ONLY THE MOST RECENT LOAD VALUES 80 Test results 80 One network for all hours 80 Separate networks for different hours 83 5.5 SUMMARY 85 CONCLUSIONS 86 Chapter Introduction Introduction 1.1 Background Load forecasting is one of the central functions in power systems operations The motivation for accurate forecasts lies in the nature of electricity as a commodity and trading article; electricity can not be stored, which means that for an electric utility, the estimate of the future demand is necessary in managing the production and purchasing in an economically reasonable way In Finland, the electricity markets have recently opened, which is increasing the competition in the field Load forecasting methods can be divided into very short-, short-, mid- and long-term models according to the time span (see for example Karanta 1990) In very-short term forecasting the prediction time can be as short as a few minutes, while in long-term forecasting it is from a few years up to several decades This work concentrates on short-term forecasting, where the prediction time varies between a few hours and about one week Short-term load forecasting (STLF) has been lately a very commonly addressed problem in power systems literature One reason is that recent scientific innovations have brought in new approaches to solve the problem The development in computer technology has broadened possibilities for these and other methods working in a realtime environment Another reason may be that there is an international movement towards greater competition in electricity markets (Räsänen 1995) Even if many forecasting procedures have been tested and proven successful, none has achieved a strong stature as a generally applied method A reason is that the circumstances and requirements of a particular situation have a significant influence on choosing the appropriate model The results presented in the literature are usually not directly comparable to each other A majority of the recently reported approaches are based on neural network techniques (see section 3.3) Many researchers have presented good results The attraction of the methods lies in the assumption that neural networks are able to learn properties of the load, which would otherwise require careful analysis to discover Chapter Introduction However, the development of the methods is not finished, and the lack of comparative results on different model variations is a problem Therefore, to make use of the techniques in a real application, a comparative analysis of the properties of different model types seems necessary 1.2 Purpose of the work This work studies the applicability of different neural network models on short-term load forecasting The approach is comparative The models are divided into two classes: models forecasting the load for one whole day at a time, and models forecasting ahead hour by hour Testing is carried out on the real load data of a Finnish electric utility The objective is to accomplish suggestions on choosing the most appropriate model(s) As there is need to forecast the load accurately at all time spans, another goal is to study the performance of the models for different lead-times Intuitively, it seems possible that different models should be preferred for different time spans even within the short-term forecasting range The work provides the basis for an automatic forecasting application to be used in a real-time environment The requirements for this are derived from its intended use within an energy management system (EMS) developed by ABB Power Oy There are some properties, which are considered important: - The model should be automatic and able to adapt quickly to changes in the load behavior - The model is intended for use in many different cases This means that generality is desired - Updating the forecast with new available data should be possible The hours closest to the forecasting time should always be forecast as accurately as possible - The model should be reliable Even exceptional circumstances must not give rise to unreasonable forecasts - Difficult weather conditions typical in Finland, especially large variation of outdoor temperature, should be taken care of - The model should be easily attachable to an energy management system Chapter Introduction This work does not study the forecasting for special days, such as religious and legal holidays Special days have different consumption profiles from ordinary days, which makes forecasting very difficult for them When implementing a real application, a means to take these days into account has to be found The most common approach, but not necessarily the best one, is to treat them as Sundays 1.3 Structure of the work The following chapter concentrates on the subject of load forecasting in general First, the properties of the load curve of an electric utility and different factors affecting the load are discussed Then possible approaches to the problem are considered The most popular conventional methods are shortly introduced The third chapter discusses neural network models and their use in load forecasting First, a short general introduction to neural networks is given Then, the most popular network type, the Multi-Layer Perceptron network (MLP) is described The basic idea in applying MLP based methods to the problem at hand is given A literature survey on neural network short-term load forecasting models is carried out at the end of the chapter As the models presented in the literature are either intended for forecasting the whole daily load curve at once, or forecasting the load hourly, this division is used here in testing different models The chapter four takes two approaches to forecast the daily load curve The first one uses a MLP network to forecast daily peak (maximum), valley (minimum), and average load values The shape of the load curve is predicted separately The second approach forecasts the whole load curve on the basis of the previous day In chapter five, the hourly models are taken into consideration Two different methods using MLP networks are studied The first one is intended for an arbitrary number of lead hours, and the other one only for short lead times To get a more reliable opinion on the performance of the models, a seasonal ARIMAX model is also tested for a comparative purpose Chapter Load forecasting Load forecasting 2.1 Factors affecting the load Generally, the load of an electric utility is composed of very different consumption units A large part of the electricity is consumed by industrial activities Another part is of course used by private people in forms of heating, lighting, cooking, laundry, etc Also many services offered by society demand electricity, for example street lighting, railway traffic etc Factors affecting the load depend on the particular consumption unit The industrial load is usually mostly determined by the level of the production The load is often quite steady, and it is possible to estimate its dependency on different production levels However, from the point of view of the utility selling electricity, the industrial units usually add uncertainty in the forecasts The problem is the possibility of unexpected events, like machine breakdowns or strikes, which can cause large unpredictable disturbances in the load level In the case of private people, the factors determining the load are much more difficult to define Each person behaves in his own individual way, and human psychology is involved in each consumption decision Many social and behavioral factors can be found For example, big events, holidays, even TV-programs, affect the load (Gross and Galiana 1987, Karanta 1990, Kim et al 1995) The weather is the most important individual factor, the reason largely being the electric heating of houses, which becomes more intensive as the temperature drops (Kallio 1985) As a large part of the consumption is due to private people and other small electricity customers, the usual approach in load forecasting is to concentrate on the aggregate load of the whole utility This is also the approach taken in this work This reduces the number of factors that can be taken into account, the most important being (Gross and Galiana, 1987): - In the short run, the meteorological conditions cause large variation in this aggregated load In addition to the temperature, also wind speed, cloud cover, and humidity have an influence (see, e.g., Chow and Leung 1996, Kallio 1985, Khotanzad et al 1996) 10 ... neural network models on short-term load forecasting The approach is comparative The models are divided into two classes: models forecasting the load for one whole day at a time, and models forecasting. .. peak load models only forecast the daily peak loads, and load shape models forecast load values for all hours (or half-hours) This work concentrates on load shape models, although peak load forecasting. .. on neural network short-term load forecasting models is carried out at the end of the chapter As the models presented in the literature are either intended for forecasting the whole daily load