Electricity price forecasting

Electricity price forecasting (EPF) is a branch of energy forecasting which focuses on predicting the spot and forward prices in wholesale electricity markets. Over the last 15 years electricity price forecasts have become a fundamental input to energy companies’ decision-making mechanisms at the corporate level. Since the early 1990s, the process of deregulation and the introduction of competitive electricity markets have been reshaping the landscape of the traditionally monopolistic and government-controlled power sectors. Throughout Europe, North America and Australia, electricity is now traded under market rules using spot and derivative contracts. However, electricity is a very special commodity: it is economically non-storable and power system stability requires a constant balance between production and consumption. At the same time, electricity demand depends on weather (temperature, wind speed, precipitation, etc.) and the intensity of business and everyday activities (, weekd

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Cluster-based Aggregate Forecasting for Residential Electricity Demand using Smart Meter Data

Karl Aberer, Samuel François Roger Joseph Humeau, Matteo Vasirani, Tri Kurniawan Wijaya

While electricity demand forecasting literature has focused on large, industrial, and national demand, this paper focuses on short-term (1 and 24 hour ahead) electricity demand forecasting for residential customers at the individual and aggregate level. Since electricity consumption behavior may vary between households, we first build a feature universe, and then apply Correlation-based Feature Selection to select features relevant to each household. Additionally, smart meter data can be used to obtain aggregate forecasts with higher accuracy using the so-called Cluster-based Aggregate Forecasting (CBAF) strategy, i.e., by first clustering the households, forecasting the clusters' energy consumption separately, and finally aggregating the forecasts. We found that the improvement provided by CBAF depends not only on the number of clusters, but also more importantly on the size of the customer base.

2015

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Individual, Aggregate, and Cluster-based Aggregate Forecasting of Residential Demand

Karl Aberer, Samuel François Roger Joseph Humeau, Matteo Vasirani, Tri Kurniawan Wijaya

Pervasive installation of smart meters opens new possibilities for advanced analytics of electricity consumption at the level of the individual household. One of the important tasks in various Smart Grid applications, from demand-response to emergency management, is the short-term electricity load forecasting at different scales, from an individual customer to a whole portfolio of customers. In this work we perform a quantitative evaluation of different machine learning methods for short-term (1 hour ahead and 24 hour ahead) electricity load forecasting at the individual and aggregate level. We discuss the relevant features that best help to improve forecasting accuracy, as well as the effectiveness of exploiting correlations between different customers. Furthermore, for aggregate forecast, we explore different clustering techniques that can be used to segment the whole customer portfolio and show that forecasting each cluster separately and then aggregating the forecast produces better accuracy compared to the traditional approach (which forecast directly the aggregate load). We also found that the improvement provided by this strategy changes as a function of total customers.

EPFL2014

Chargement

AUSTERITY IN THE AFTERMATH OF THE GREAT RECESSION

Christian Pröbsting

We examine austerity in advanced economies since the Great Recession. Austerity shocks are reductions in government purchases that exceed reduced-form forecasts. Austerity shocks are statistically associated with lower real GDP, lower inflation and higher net exports. We estimate a cross-sectional multiplier of roughly 2. A multi-country DSGE model calibrated to 29 advanced economies generates a multiplier consistent with the data. Counterfactuals suggest that eliminating austerity would have substantially reduced output losses in Europe. Austerity shocks were sufficiently contractionary that debt-to-GDP ratios in some European countries increased as a consequence of endogenous reductions in GDP and tax revenue

NBER2017

Chargement

FIN-407: Financial econometrics

This course aims to give an introduction to the application of machine learning to finance. These techniques gained popularity due to the limitations of traditional financial econometrics methods tackling big data. We will review and compare traditional methods and machine learning algorithms.

MGT-418: Convex optimization

This course introduces the theory and application of modern convex optimization from an engineering perspective.