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.
Deep learning detects inaccurate smart meters for resource savings.
Electric vehicles (EVs) have been gaining popularity due to their environmental friendliness and efficiency. EV charging station networks are scalable solutions for supporting increasing numbers of EVs within modern electric grid constraints, yet few tools exist to aid the physical configuration design of new networks.…
Crowdsourcing has been successfully applied in many domains including astronomy, cryptography and biology. In order to test its potential for useful application in a Smart Grid context, this paper investigates the extent to which a crowd can contribute predictive hypotheses to a model of residential electric energy con…
Paper proposes transparent reporting of algorithmic energy usage to promote environmental sustainability.
Challenge forecasts EV charging station usage accurately.
A new method for time-series data provides guaranteed coverage and adapts to non-exchangeable data.
Adopting a zonal structure of electricity market requires specification of zones' borders. In this paper we use social welfare as the measure to assess quality of various zonal divisions. The social welfare is calculated by Market Coupling algorithm. The analyzed divisions are found by the usage of extended Locational …
Traditional load analysis is facing challenges with the new electricity usage patterns due to demand response as well as increasing deployment of distributed generations, including photovoltaics (PV), electric vehicles (EV), and energy storage systems (ESS). At the transmission system, despite of irregular load behavio…
Framework identifies comorbidities for frequent ED and inpatient visits.
The authors characterize flexibility in power and energy markets considering time, spatiality, resource, and risk.
Power grids are one of the most important components of infrastructure in today's world. Every nation is dependent on the security and stability of its own power grid to provide electricity to the households and industries. A malfunction of even a small part of a power grid can cause loss of productivity, revenue and i…
Non-intrusive load monitoring (NILM), also known as energy disaggregation, is a blind source separation problem where a household's aggregate electricity consumption is broken down into electricity usages of individual appliances. In this way, the cost and trouble of installing many measurement devices over numerous ho…
Study electric field and potential of torus knots, focusing on z-axis.
Deep neural networks predict electricity consumption accurately.
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…
Paper proposes an unsupervised NILM framework using GLDA for diverse utility data.
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.
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 proves a new theorem linking mass and electric charge for certain types of manifolds.
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…
Model predicts cognitive health risks based on smartphone usage patterns.
ElecSim models long-term electricity planning with agent-based Monte-Carlo simulations.
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.
Active learning reduces smart meter data needs for better electric load predictions.
Proposes a model for long-term electricity contracts with explicit computation and easy calibration.
Novel probabilistic models forecast residential heating and electricity demand at hourly resolution.
Adaptive models improve electricity demand forecasting during lockdown.