Yannig Goude

Local Short and Middle Term Electricity Load Forecasting With Semi-Parametric Additive Models

Electricity load forecasting faces rising challenges due to the advent of innovating technologies such as smart grids, electric cars and renewable energy production. For distribution network managers, a good knowledge of the future electricity consumption stands as a central point for the reliability of the network and investment strategies. In this paper, we suggest a semi-parametric approach based on generalized additive models theory to model electrical load over more than 2200 substations of the French distribution network, and this at both short and middle term horizons. These generalized additive models estimate the relationship between load and the explanatory variables: temperatures, calendar variables, etc. This methodology has been applied with good results on the French grid. In addition, we highlight the fact that the estimated functions describing the relations between demand and the driving variables are easily interpretable, and that a good temperature prediction is important.

Modeling and Forecasting Daily Electricity Load Curves: A Hybrid Approach

We propose a hybrid approach for the modeling and the short-term forecasting of electricity loads. Two building blocks of our approach are (1) modeling the overall trend and seasonality by fitting a generalized additive model to the weekly averages of the load and (2) modeling the dependence structure across consecutive daily loads via curve linear regression. For the latter, a new methodology is proposed for linear regression with both curve response and curve regressors. The key idea behind the proposed methodology is dimension reduction based on a singular value decomposition in a Hilbert space, which reduces the curve regression problem to several ordinary (i.e., scalar) linear regression problems. We illustrate the hybrid method using French electricity loads between 1996 and 2009, on which we also compare our method with other available models including the Électricité de France operational model. Supplementary materials for this article are available online.

Forecasting Electricity Consumption by Aggregating Experts; How to Design a Good Set of Experts

Short-term electricity forecasting has been studied for years at EDF and different forecasting models were developed from various fields of statistics or machine learning (functional data analysis, time series, non-parametric regression, boosting, bagging). We are interested in the forecasting of France’s daily electricity load consumption based on these different approaches. We investigate in this empirical study how to use them to improve prediction accuracy. First, we show how combining members of the original set of forecasts can lead to a significant improvement. Second, we explore how to build various and heterogeneous forecasts from these models and analyze how we can aggregate them to get even better predictions.

Massive-Scale Simulation of Electrical Load in Smart Grids Using Generalized Additive Models

The emergence of Smart Grids is posing a wide range of challenges for electric utility companies and network operators: Integration of non-dispatchable power from renewable energy sources (e.g., photovoltaics, hydro and wind), fundamental changes in the way energy is consumed (e.g., due to dynamic pricing, demand response and novel electric appliances), and more active operations of the networks to increase efficiency and reliability. A key in managing these challenges is the ability to forecast network loads at low levels of locality, e.g., counties, cities, or neighbourhoods. Accurate load forecasts improve the efficiency of supply as they help utilities to reduce operating reserves, act more efficiently in the electricity markets, and provide more effective demand-response measures. In order to prepare for the Smart Grid era, there is a need for a scalable simulation environment which allows utilities to develop and validate their forecasting methodology under various what-if-scenarios. This paper presents a massive-scale simulation platform which emulates electrical load in an entire electrical network, from Smart Meters at individual households, over low- to medium-voltage network assets, up to the national level. The platform supports the simulation of changes in the customer portfolio and the consumers’ behavior, installment of new distributed generation capacity at any network level, and dynamic reconfigurations of the network. The paper explains the underlying statistical modeling approach based on Generalized Additive Models, outlines the system architecture, and presents a number of realistic use cases that were generated using this platform.

Electricity Forecasting Using Multi-Stage Estimators of Nonlinear Additive Models

French electricity load forecasting has encountered major changes during the past decade. These changes are, among other things, due to the opening of the electricity market and the economic crisis, which require the development of new automatic time adaptive prediction methods. The advent of innovating technologies also needs the development of some automatic methods because thousands or tens of thousands of time series have to be studied. In this paper we adopt for prediction a semi-parametric approach based on additive models. We present an automatic procedure for explanatory variable selection in an additive model and show how to correct middle term forecasting errors for short term forecasting. First, we consider an application to the EDF customer load demand which is typical of a load demand at an aggregate level. The goal of the application is to select variables from a large explanatory variables dictionary. The second application presented is an application on load demand of GEFCom 2012 competition, which we consider as a local application, where a major difficulty is to select some meteorological stations.

Automatic Component Selection in Additive Modeling of French National Electricity Load Forecasting

We consider estimation and model selection in sparse high-dimensional linear additive models when multiple covariates need to be modeled nonparametrically, and propose some multi-step estimators based on B-splines approximations of the additive components. In such models, the overall number of regressors d can be large, possibly much larger than the sample size n. However, we assume that there is a smaller than n number of regressors that capture most of the impact of all covariates on the response variable. Our estimation and model selection results are valid without assuming the conventional “separation condition”—namely, without assuming that the norm of each of the true nonzero components is bounded away from zero. Instead, we relax this assumption by allowing the norms of nonzero components to converge to zero at a certain rate. The approaches investigated in this paper consist of two steps. The first step implements the variable selection, typically by the Group Lasso, and the second step applies a penalized P-splines estimation to the selected additive components. Regarding the model selection task we discuss, the application of several criteria such as Akaike information criterion (AIC), Bayesian information criterion (BIC), and generalized cross validation (GCV) and study the consistency of BIC, i.e. its ability to select the true model with probability converging to 1. We then study post-model estimation consistency of the selected components. We end the paper by applying the proposed procedure on some real data related to electricity load consumption forecasting: the EDF (Electricite de France) portfolio.

Spatial estimation of electricity consumption using socio-demographic information

Electric power consumption is known at fine temporal scales (e.g. hourly) for geographical zones corresponding to the electric network service divisions (e.g. substations). In several applications, there is a strong need to estimate the past or to forecast future consumption at different divisions, for example the town, district or city block levels. The deployment of smart meters only gives a partial answer to this problem because they usually do not provide exhaustive measures at such temporal scales. We propose in this paper a generic approach to estimate electric consumption on any geographical zones from source zones where fine-grained consumption data is available, using in addition socio-demographic information. The approach is evaluated on both real and simulated data.

Disaggregated electricity forecasting using wavelet-based clustering of individual consumers

Electricity load forecasting is crucial for utilities for production planning as well as marketing offers. Recently, the increasing deployment of smart grids infrastructure requires the development of more flexible data driven forecasting methods adapting quite automatically to new data sets. We propose to build clustering tools useful for forecasting the load consumption. The idea is to disaggregate the global signal in such a way that the sum of disaggregated forecasts significantly improves the prediction of the whole global signal. The strategy is in three steps: first we cluster curves defining super-consumers, then we build a hierarchy of partitions within which the best one is finally selected with respect to a disaggregated forecast criterion. The proposed strategy is applied to a dataset of individual consumers from the French electricity provider EDF. A substantial gain of 16 % in forecast accuracy comparing to the 1 cluster approach is provided by disaggregation while preserving meaningful classes of consumers.