Paper optimizes energy trading on DA markets using RL.
problem Volatility and randomness in renewable energy sources.
method Markov Decision Process, reinforcement learning, evolutionary algorithm.
result RL-based strategy generates highest market profits.
Optimal energy trading strategy for intraday markets using Hawkes processes.
problem Optimal execution in intraday energy markets with specific trading patterns.
method Calibrated Hawkes process model with transient price impact.
result Substantial cost reductions in TWAP and VWAP benchmarks.
Dual model combines HMM and neural networks for energy trading during volatile periods.
problem Optimizing energy trading performance during market volatility.
method Integrates Hidden Markov Models and neural networks with Black-Litterman portfolio optimization.
result Achieved 83% return with Sharpe ratio 0.77 during COVID period.
One major hurdle in the road toward a low carbon economy is the present entanglement of developed economies with oil. This tight relationship is mirrored in the correlation between most of economic indicators with oil price. This paper addresses the role of oil compared to the other three main energy commodities -coal,…
Deep reinforcement learning improves trading performance in volatile energy markets.
problem Volatility and low signal-to-noise ratios in energy markets.
method Formalized trading as a stochastic system, developed reactive and adaptive algorithms, used deep neural networks.
result Deep reinforcement learning models outperform buy-and-hold strategy with an 83% higher Sharpe ratio.
We study the optimal trading policies for a wind energy producer who aims to sell the future production in the open forward, spot, intraday and adjustment markets, and who has access to imperfect dynamically updated forecasts of the future production. We construct a stochastic model for the forecast evolution and deter…
Hybrid approach improves probabilistic forecasts for electricity trading.
problem Improving probabilistic forecasts for electricity trading markets.
method Combines QRA and factor-based averaging for probabilistic forecasting.
result The hybrid approach outperforms benchmarks in statistical measures and economic value.
Study optimizes SREC generation and trading in solar energy markets.
problem Optimizing solar energy generation and trading in SREC markets.
method Mean-field game approach to solve stochastic game with heterogeneous agents.
result Characterized firms' optimal controls and equilibrium SREC price.
Recent innovations in Information and Communication Technologies (ICT) provide new opportunities and challenges for integration of distributed energy resources (DERs) into the energy supply system as active market players. By increasing integration of DERs, novel market platform should be designed for these new market …
A framework uses deep reinforcement learning to optimize energy storage in intraday markets.
problem Optimizing energy storage in intraday markets for renewable energy integration.
method Markov Decision Process, asynchronous distributed fitted Q iteration algorithm, artificial trajectories.
result The agent converges to a policy that achieves higher total revenues than the benchmark strategy.
The paper analyzes profitable bidding strategies for BESS in day-ahead and intraday markets.
problem Optimizing profitability of Battery Energy Storage Systems (BESS) in day-ahead and intraday markets.
method Employing the rolling intrinsic approach to model continuous intraday markets, accounting for bid-ask spreads and liquidity constraints.
result Multi-market bidding strategies outperform single-market participation, and relaxing daily cycling constraints can unlock additional value.
Optimizes intraday electricity trading to minimize costs.
problem Minimizing costs in intraday electricity trading.
method Derives an optimal model considering order book depth, time to delivery, and trading regimes.
result Optimal execution strategies have a significant monetary impact.
Dynamic probabilistic forecasts guide optimal decisions in uncertain processes.
problem Optimal decision making in processes influenced by uncertain random factors.
method Stochastic models for probabilistic forecast evolution, calibrated from ensemble forecasts.
result Optimal decision strategies determined using dynamic probabilistic forecasts.
Paper presents a novel nonparametric method to price Asian options.
problem Difficulty in pricing Asian options, especially with arithmetic average price.
method Nonparametric Predictive Inference (NPI) for Asian option pricing.
result NPI method provides a more precise and uncertain prediction of future asset prices.
High-frequency trading strategy boosts battery storage profits.
problem Maximizing revenue for battery energy storage systems in intraday markets.
method Adapted dynamic programming for continuous intraday markets, considering limit order book dynamics.
result Dynamic programming strategy outperforms standard re-optimization methods, increasing profits by 58% and 14% respectively.
SmartExchange trades memory for computation in neural networks.
problem Energy-efficient inference of deep neural networks.
method Algorithm-hardware co-design framework integrating sparsification, decomposition, and quantization.
result SmartExchange reduces energy consumption and improves performance.
SREC markets are a relatively novel market-based system to incentivize the production of energy from solar means. A regulator imposes a floor on the amount of energy each regulated firm must generate from solar power in a given period and provides them with certificates for each generated MWh. Firms offset these certif…
Proposes a new acquisition function for batched Bayesian optimization.
problem Intractability of acquisition functions for batched Bayesian optimization.
method Statistical physics inspired acquisition function for Gaussian processes.
result Demonstrates competitive performance on various problems.
Properties of distributions of the number of trades in different intraday time intervals for five stocks traded in MICEX are studied. The dependence of the mean number of trades on the capital turnover is analyzed. Correlation analysis using factorial and Hq moments demonstrates the multifractal nature of these dist…
Develops a new trading strategy for renewable producers to manage price volatility.
problem Price volatility and imbalance risk in power markets due to renewable generation.
method Data-driven continuous-time stochastic optimal control framework using SDEs and diffusion models.
result Trading strategy outperforms benchmarks and reduces profit and loss.
Many recent models of trade dynamics use the simple idea of wealth exchanges among economic agents in order to obtain a stable or equilibrium distribution of wealth among the agents. In particular, a plain analogy compares the wealth in a society with the energy in a physical system, and the trade between agents to the…
Robinhood users react strongly to overnight price changes and big losers, trading quickly after extreme losses.
problem Understanding trading behavior of Robinhood users, especially in high-frequency trading scenarios.
method Analyzed intraday and overnight price changes, focusing on big losers and gainers.
result Robinhood users react more to overnight price changes and big losers, trading quickly after extreme losses.
Paper extends multivariate rank tests for robust subspace detection.
problem Testing distributional similarity in multivariate data.
method Soft and subspace robust multivariate rank tests based on entropy regularized optimal transport.
result Trade-off between detection power and false alarm rate via projections.
Proposes a neural network for efficient imbalance electricity price forecasting.
problem Accurate and efficient imbalance electricity price forecasting in industrial energy trading systems.
method Market-rule-informed neural network framework.
result The proposed model achieves competitive forecasting performance with fewer parameters and shorter training time.
In this paper we develop a statistical arbitrage trading strategy with two key elements in hi-frequency trading: stop-loss and leverage. We consider, as in Bertram (2009), a mean-reverting process for the security price with proportional transaction costs; we show how to introduce stop-loss and leverage in an optimal t…
Short-term probabilistic forecasting of German electricity imbalance prices.
problem Uncertainty in renewable energy capacity and electricity prices.
method Combining lasso with bootstrap, gamlss, and probabilistic neural networks for forecasting imbalance prices.
result Sophisticated methods improve empirical coverage of imbalance prices but do not substantially outperform the intraday continuous price index.
We provide an exact solution to the ideal-gas-like models studied in econophysics to understand the microscopic origin of Pareto-law. In these class of models the key ingredient necessary for having a self-organized scale-free steady-state distribution is the trading or collision rule where agents or particles save a d…
Investigates how 'green' labels affect bond market dynamics.
problem Understanding the impact of 'green' labels on bond market trading activity.
method Used Hawkes processes and a moving average model to analyze high-frequency bond price dynamics.
result Differences in bond market dynamics emerge during periods with interest rate announcements, especially for energy market issuers.
This paper applies quantum probability theory to model asset returns, avoiding assumptions about quantum effects.
problem Modeling asset returns with classical probability theory.
method Derives a Schrödinger-like trading equation using quantum probability, linking it to traders' decisions and market behaviors.
result Quantum probability can describe multimodal distributions of asset returns without assuming quantum effects.
New RBM method for missing data inference, comparing performance to existing methods.
problem Missing data inference and perception-distortion trade-off.
method Linearization of RBM effective energy function for missing data.
result Proposed method outperforms existing reconstruction procedures in missing data inference.
This review compares various deep generative models.
problem Training deep neural networks to model data distributions.
method Comprehensive comparison of VAEs, GANs, flows, energy models, and autoregressives.
result Trade-offs and interrelationships among different models.
Motivated by how transaction amount constrain trading volume and price volatility in stock market, we, in this paper, study the relation between volume and price if amount of transaction is given. We find that accumulative trading volume gradually emerges a kurtosis near the price mean value over a trading price range …
Machine learning software accounts for a significant amount of energy consumed in data centers. These algorithms are usually optimized towards predictive performance, i.e. accuracy, and scalability. This is the case of data stream mining algorithms. Although these algorithms are adaptive to the incoming data, they have…
The free energy functional has recently been proposed as a variational principle for bounded rational decision-making, since it instantiates a natural trade-off between utility gains and information processing costs that can be axiomatically derived. Here we apply the free energy principle to general decision trees tha…
Optimizes BESS for cross-market energy arbitrage to boost revenues.
problem Charging and discharging BESS at optimal times to maximize profits.
method Developed a generic framework, backtest engine, and optimization strategy.
result Boosted revenues by 10% through strategic BESS operation.
This paper analyzes energy and carbon footprints in distributed and federated learning.
problem High energy costs and carbon emissions in centralized AI methods.
method A novel framework quantifying energy and carbon footprints in vanilla and consensus-based FL methods.
result Optimal bounds and operational points for green FL designs and sustainability assessment.
Paper improves DNN accelerator robustness against bit errors with energy savings.
problem Bit errors in quantized DNN weights reduce energy efficiency.
method Combines robust fixed-point quantization, weight clipping, and random bit error training.
result Significantly improves robustness against random bit errors with high energy savings.
Discriminatory trade liberalization policies are becoming more popular among world economies. Countries are motivated to enter for regional trade agreements to capture faster economic growth for alleviating poverty. In developing economies like most of the member countries of the Association of South East Asian Nations…
Trading affects grid frequency fluctuations, making them more extreme.
problem Impact of trading on grid frequency stability.
method Analysis of frequency time series from 2011 and 2017.
result Trading modifies frequency fluctuation statistics, making large deviations more likely.
Inspired from recent insights into the common ground of machine learning, optimization and decision-making, this paper proposes an easy-to-implement, but effective procedure to enhance both the quality of renewable energy forecasts and the competitive edge of renewable energy producers in electricity markets with a dua…
Domain Adaptation in 6G wireless networks: When is it green?
problem Energy consumption of Domain Adaptation (UDA) compared to single-task training in 6G wireless networks.
method Investigate energy consumption and propose a method to determine the minimum number of target domains for UDA to be more energy-efficient than retraining.
result Proposed a method to determine the minimum number of target domains for UDA to be more energy-efficient than retraining.
Paper optimizes battery storage in multiple energy markets for better profits.
problem Optimizing battery storage participation in multiple energy markets to balance supply and demand.
method Developed a joint bidding strategy combining intraday and frequency markets using mixed integer linear programming and a learned classifier strategy.
result The LCS increases overall profits by over 4% compared to static strategies and by more than 3% over a naive dynamic benchmark.
QubitHD improves HD computing ML efficiency without sacrificing accuracy.
problem Trade-off between energy efficiency and classification accuracy in HD computing-based ML.
method Stochastically binarizes HD-based algorithms while maintaining comparable classification accuracies.
result 65% improvement in energy efficiency and 95% improvement in training time on FPGA.
"How much energy is consumed for an inference made by a convolutional neural network (CNN)?" With the increased popularity of CNNs deployed on the wide-spectrum of platforms (from mobile devices to workstations), the answer to this question has drawn significant attention. From lengthening battery life of mobile device…
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…
We introduce a new online learning framework where, at each trial, the learner is required to select a subset of actions from a given known action set. Each action is associated with an energy value, a reward and a cost. The sum of the energies of the actions selected cannot exceed a given energy budget. The goal is to…
In this paper we study the pricing and hedging of structured products in energy markets, such as swing and virtual gas storage, using the exponential utility indifference pricing approach in a general incomplete multivariate market model driven by finitely many stochastic factors. The buyer of such contracts is allowed…
TradeR uses RL to execute trades in real markets, minimizing surprise and catastrophe.
problem Minimizing surprise and catastrophe in high-frequency trading.
method Hierarchical RL with energy-based surprise value function.
result TradeR outperforms in abrupt price changes and maintains profitability.