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.
ElecSim models long-term electricity planning with agent-based Monte-Carlo simulations.
Optimizes electric aircraft deployment for Canadian aviation to reduce emissions.
New method for selecting clusters in residential electricity data.
Paper uses econometrics time series model with T-student Distribution for short-term load forecasting.
Fast, interpretable electricity consumption scenarios for individual consumers.
Active learning reduces smart meter data needs for better electric load predictions.
Study integrates reliability constraints into generation planning models.
Novel probabilistic models forecast residential heating and electricity demand at hourly resolution.
Paper proposes a risk-averse approach to energy storage price arbitrage using conformal uncertainty quantification.
The energy transition is well underway in most European countries. It has a growing impact on electric power systems as it dramatically modifies the way electricity is produced. To ensure a safe and smooth transition towards a pan-European electricity production dominated by renewable sources, it is of paramount import…
Paper models uncertainty in electricity and gas markets to assess its impact.
Model uses GAMs to forecast hourly electricity load weeks to one year ahead.
Future grid scenario analysis requires a major departure from conventional power system planning, where only a handful of most critical conditions is typically analyzed. To capture the inter-seasonal variations in renewable generation of a future grid scenario necessitates the use of computationally intensive time-seri…
New method quantifies resilience of electric distribution systems from historical data.
Recently there has been significant research on power generation, distribution and transmission efficiency especially in the case of renewable resources. The main objective is reduction of energy losses and this requires improvements on data acquisition and analysis. In this paper we address these concerns by using con…
We propose a framework for general probabilistic multi-step time series regression. Specifically, we exploit the expressiveness and temporal nature of Sequence-to-Sequence Neural Networks (e.g. recurrent and convolutional structures), the nonparametric nature of Quantile Regression and the efficiency of Direct Multi-Ho…
Study electric field and potential of torus knots, focusing on z-axis.
Accurate forecasts of electricity spot prices are essential to the daily operational and planning decisions made by power producers and distributors. Typically, point forecasts of these quantities suffice, particularly in the Nord Pool market where the large quantity of hydro power leads to price stability. However, wh…
European steel industry shifts to electric arc furnaces, reducing scrap imports and increasing competition.
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…
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…
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…
A new method solves complex hydroelectricity planning problems.
Study shows Bitcoin mining with surplus electricity can boost KEPCO's financial stability.
Paper provides a method to price electricity storage contracts using COS technique.
PSQRNN model forecasts electricity consumption in China by integrating neural networks and quantile regression.
Algorithm optimizes electricity procurement costs by 1.65%.
In this paper, we analyze Nash equilibria between electricity producers selling their production on an electricity market and buying CO2 emission allowances on an auction carbon market. The producers' strategies integrate the coupling of the two markets via the cost functions of the electricity production. We set out a…
In this note, we present an existence result of a Nash equilibrium between electricity producers selling their production on an electricity market and buying CO2 emission allowances on an auction carbon market. The producers' strategies integrate the coupling of the two markets via the cost functions of the electricity…
Proposes a pricing agent using reinforcement learning to balance renewable energy demand.
Paper models and forecasts intra-day electricity price spreads.
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…
Electricity accounts for 25% of global greenhouse gas emissions. Reducing emissions related to electricity consumption requires accurate measurements readily available to consumers, regulators and investors. In this case study, we propose a new real-time consumption-based accounting approach based on flow tracing. This…
We analyze long-term memory properties of hourly prices of electricity in the Czech Republic between 2009 and 2012. As the dynamics of the electricity prices is dominated by cycles -- mainly intraday and daily -- we opt for the detrended fluctuation analysis, which is well suited for such specific series. We find that …
Study predicts electricity prices using LSTM models with feature selection, considering market coupling.
Paper proposes online learning for estimating AC network admittance matrix.
How does dynamic price information flow among Northern European electricity spot prices and prices of major electricity generation fuel sources? We use time series models combined with new advances in causal inference to answer these questions. Applying our methods to weekly Nordic and German electricity prices, and oi…
Transfer learning improves electricity price forecasting accuracy.
The price of electricity is far more volatile than that of other commodities normally noted for extreme volatility. The possibility of extreme price movements increases the risk of trading in electricity markets. However, underlying the process of price returns is a strong mean-reverting mechanism. We study this featur…
The paper simplifies electricity market curves with less parameters.
In our paper we analyze the relationship between the day-ahead electricity price of the Energy Exchange Austria (EXAA) and other day-ahead electricity prices in Europe. We focus on markets, which settle their prices after the EXAA, which enables traders to include the EXAA price into their calculations. For each market…
Transformer model forecasts electricity price spread for virtual bidding.
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…
New approach to electric group for knots and links.
Plants sense their environment by producing electrical signals which in essence represent changes in underlying physiological processes. These electrical signals, when monitored, show both stochastic and deterministic dynamics. In this paper, we compute 11 statistical features from the raw non-stationary plant electric…
Novel neural network predicts electricity prices with higher moments.
Model for hedging price and quantity risks in electricity markets.
Model detects electricity theft with high accuracy.