A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Novel probabilistic models forecast residential heating and electricity demand at hourly resolution.
Proposes a pricing agent using reinforcement learning to balance renewable energy demand.
Considering the interdependencies between water and electricity use is critical for ensuring conservation measures are successful in lowering the net water and electricity use in a city. This water-electricity demand nexus will become even more important as cities continue to grow, causing water and electricity utiliti…
Paper proposes a method for predicting any quantile of short-term electricity demand.
AutoML improves electricity demand forecasting models.
Our paper aims to model supply and demand curves of electricity day-ahead auction in a parsimonious way. Our main task is to build an appropriate algorithm to present the information about electricity prices and demands with far less parameters than the original one. We represent each curve using mesh-free interpolatio…
Paper optimizes demand aggregation for low-level electricity markets.
In this paper, we formulate a method for minimising the expectation value of the procurement cost of electricity in two popular spot markets: {\it day-ahead} and {\it intra-day}, under the assumption that expectation value of unit prices and the distributions of prediction errors for the electricity demand traded in tw…
With developing of computation tools in the last years, data analysis methods to find insightful information are becoming more common among industries and researchers. This paper is the first part of the times series analysis of New England electricity price and demand to find anomaly in the data. In this paper time-se…
Study forecasts monthly electricity demand using pattern similarity-based methods.
The paper forecasts joint electricity demand across 14 British regions using additive models.
Adaptive models improve electricity demand forecasting during lockdown.
New method for disaggregate electricity demand forecasting at household level.
This research improves LSTM for monthly electricity demand forecasting using pattern-based methods.
A winning method for day-ahead electricity demand forecasting during and after the COVID-19 pandemic.
Proposes a new method for predicting uncertain net electricity demand.
Novel time series forecasting method using sliding window signatures.
In this paper, we study the price responsiveness of electricity consumption from empirical commercial and industrial load data obtained from Texas. Employing a dynamical system perspective, we show that price responsive demand can be modeled as a hybrid of a Hammerstein model with delay following a price surge, and a l…
A new method forecasts hourly electricity prices considering product dynamics and limit order book signals.
Model forecasts hourly electricity demand influenced by weather, socio-economic, and political factors.
We consider the problem of optimal trading for a power producer in the context of intraday electricity markets. The aim is to minimize the imbalance cost induced by the random residual demand in electricity, i.e. the consumption from the clients minus the production from renewable energy. For a simple linear price impa…
Energy policy in Europe has been driven by the three goals of security of supply, economic competitiveness and environmental sustainability, referred to as the energy trilemma. Although there are clear conflicts within the trilemma, member countries have acted to facilitate a fully integrated European electricity marke…
We introduce a new and highly tractable structural model for spot and derivative prices in electricity markets. Using a stochastic model of the bid stack, we translate the demand for power and the prices of generating fuels into electricity spot prices. The stack structure allows for a range of generator efficiencies p…
Adaptive probabilistic load forecasting improves performance in power systems.
Electricity consumption has increased exponentially during the past few decades. This increase is heavily burdening the electricity distributors. Therefore, predicting the future demand for electricity consumption will provide an upper hand to the electricity distributor. Predicting electricity consumption requires man…
Model for hedging price and quantity risks in electricity markets.
Electricity is bought and sold in wholesale markets at prices that fluctuate significantly. Short-term forecasting of electricity prices is an important endeavor because it helps electric utilities control risk and because it influences competitive strategy for generators. As the "smart grid" grows, short-term price fo…
CapOptix uses options theory to price capacity in electricity markets.
Intra-day price spreads are of interest to electricity traders, storage and electric vehicle operators. This paper formulates dynamic density functions, based upon skewed-t and similar representations, to model and forecast the German electricity price spreads between different hours of the day, as revealed in the day-…
Paper uses CVAE to simulate tariff impacts on electricity consumption.
Study improves forecasting of aggregated curves in electricity markets.
Research focuses on predicting electricity prices with complex models considering probabilistic forecasts.
Active learning reduces smart meter data needs for better electric load predictions.
This paper proposes an agent-based model that combines both spot and balancing electricity markets. From this model, we develop a multi-agent simulation to study the integration of the consumers' flexibility into the system. Our study identifies the conditions that real-time prices may lead to higher electricity costs,…
Optimizes electric aircraft deployment for Canadian aviation to reduce emissions.
This paper extends exponential smoothing to distributional time series using Wasserstein distance.
The non-storability of electricity makes it unique among commodity assets, and it is an important driver of its price behaviour in secondary financial markets. The instantaneous and continuous matching of power supply with demand is a key factor explaining its volatility. During periods of high demand, costlier generat…
A federated graph learning approach improves EV charging demand forecasting while protecting against cyberattacks.
The liberalization of electricity markets and the development of renewable energy sources has led to new challenges for decision makers. These challenges are accompanied by an increasing uncertainty about future electricity price movements. The increasing amount of papers, which aim to model and predict electricity pri…
State-space models win a forecasting competition for unstable data.
Optimizes bidding in hourly and quarter-hourly electricity markets to reduce price impact.
Our paper aims to model and forecast the electricity price by taking a completely new perspective on the data. It will be the first approach which is able to combine the insights of market structure models with extensive and modern econometric analysis. Instead of directly modeling the electricity price as it is usuall…
Proposes a new method for multivariate functional regression.
Deep models predict intraday electricity prices accurately.
We present a comparative study of different probabilistic forecasting techniques on the task of predicting the electrical load of secondary substations and cabinets located in a low voltage distribution grid, as well as their aggregated power profile. The methods are evaluated using standard KPIs for deterministic and …
Hydropower reduces system electricity price and volatility, especially at extreme levels.
Paper proposes combining GAM and DNN for accurate peak demand estimation from lower-resolution data.
This paper formulates dynamic density functions, based upon skewed-t and similar representations, to model and forecast electricity price spreads between different hours of the day. This supports an optimal day ahead storage and discharge schedule, and thereby facilitates a bidding strategy for a merchant arbitrage fac…