Paper examines power consumption in neural networks using various activation functions.
problem Power consumption in machine learning models.
method Examines power consumption for different activation functions.
result Substantial differences in power consumption exist between activation functions.
Neural network predicts daily power consumption with high accuracy.
problem Middle-term power consumption prediction in the energy sector.
method Incorporates trend, seasonality, and weather conditions in a shallow Neural Network.
result Excellent density forecast results on one-year test set.
New model forecasts power consumption with high accuracy over months to years.
problem Probabilistic forecasting of power consumption in a middle-term horizon.
method Combines traditional time-series analysis with weather conditions using Gaussian Process.
result Promising results in Out-of-Sample density forecasts up to one year.
The paper develops a method for forecasting power consumption at various levels of aggregation.
problem Forecasting power consumption at different levels of household aggregation.
method Three-step process: feature generation, aggregation, and projection.
result The method provides theoretical guarantees on prediction error and performs well on real data.
Investment and consumption models show a threshold for optimal policies that converge to a steady state.
problem Optimal investment and consumption policies in financial models.
method Analytical and numerical methods to find and validate the turnpike property and convergence rate.
result Threshold value determines the turnpike property for investment policies, independent of specific utility functions.
Greedy algorithm optimizes consumption habits with power utility.
problem Optimizing lifetime consumption with habit formation under power utility.
method Developed a greedy algorithm using Monte Carlo simulation.
result Greedy solution is a good approximation to the optimal solution.
Study many-player investment-consumption games with power FPPs, finding market-risk preference affects consumption.
problem Investment and consumption optimization in a mean field competition setting.
method Solve many-player and mean field games using power FPPs, providing closed-form solutions.
result Market-risk relative consumption preference affects agent's consumption decisions.
We study the utility maximization problem for power utility random fields in a semimartingale financial market, with and without intermediate consumption. The notion of an opportunity process is introduced as a reduced form of the value process of the resulting stochastic control problem. We show how the opportunity pr…
Machine learning predicts Mars Express spacecraft's thermal power consumption.
problem Accurately predict Mars Express spacecraft's thermal power consumption for optimal operation planning.
method State-of-the-art feature engineering and machine learning methods.
result The proposed pipeline improves predictive performance and efficiency.
RL applied to TCLs for power consumption control.
problem Optimizing power consumption using TCLs with RL.
method Modelica-based reinforcement learning (Q-learning) for stochastic TCLs.
result Q-learning parameters affect controller performance.
This paper analyzes popular time-nonseparable utility functions that describe "habit formation" consumer preferences comparing current consumption with the time averaged past consumption of the same individual and "catching up with the Joneses" (CuJ) models comparing individual consumption with a cross-sectional averag…
RNN(p) improves power consumption forecasts with interpretable models.
problem Improving power consumption forecasts for energy sector decisions.
method RNN(p) models with p time lags, using structured feedbacks.
result RNN(p) models achieve excellent forecasting accuracy and interpretability.
New RL method optimizes power and accuracy for activity recognition.
problem Balancing power consumption and accuracy in wearable devices for activity recognition.
method Reinforcement Learning with multiple feedback sources for feature selection.
result Achieved good trade-off between power consumption and accuracy.
This paper evaluates power consumption and inference time for face emotion recognition on embedded systems.
problem Lack of information on power consumption and inference time for face emotion recognition on embedded systems.
method Identified state-of-the-art methods, collected new dataset, evaluated on three embedded devices.
result Gray images are more suitable for embedded systems, and power consumption and inference time are limiting factors.
Efficient neural network improves disaggregation of home energy usage.
problem Estimating power consumption of individual appliances from total home power.
method Fully convolutional neural network architecture with improved computational efficiency.
result Achieves state-of-the-art disaggregation performance with reduced training and prediction times.
Graph neural network optimizes energy-efficient precoding for massive MIMO systems.
problem Energy bottleneck in massive MIMO systems due to high DAC complexity and power consumption.
method Proposes a graph neural network to directly output precoded quantized vectors from channel matrix and transmit symbols.
result Significant increase in achievable sum rate with reduced DAC power consumption.
This paper tackles energy-efficient machine learning on low-power devices.
problem Energy consumption in machine learning due to data communication.
method Dynamic averaging for integer exponential families on low-power processors.
result Achieves comparable model quality with significantly less communication and energy.
Solves investment, consumption, and life insurance problem with capital constraints.
problem Optimal investment, consumption, and life insurance with capital constraints.
method Martingale approach to prove existence of optimal strategy and measure, explicit solutions for power utility functions.
result Explicit solutions for optimal investment, consumption, and life insurance strategies.
In this paper, a novel joint transmit power and resource allocation approach for enabling ultra-reliable low-latency communication (URLLC) in vehicular networks is proposed. The objective is to minimize the network-wide power consumption of vehicular users (VUEs) while ensuring high reliability in terms of probabilisti…
We consider the economic problem of optimal consumption and investment with power utility. We study the optimal strategy as the relative risk aversion tends to infinity or to one. The convergence of the optimal consumption is obtained for general semimartingale models while the convergence of the optimal trading strate…
Study optimal consumption and investment strategies with constraints in a market with random coefficients.
problem Optimal consumption and investment strategies with constraints in a regime switching market with random coefficients.
method Explicit optimal strategies provided via solutions to new BSDE systems.
result Solving new BSDEs to find optimal values and strategies.
We consider a model of optimal investment and consumption with both habit formation and partial observations in incomplete Itô processes market. The investor chooses his consumption under the addictive habits constraint while only observing the market stock prices but not the instantaneous rate of return. Applying the …
High error rates improve neural network performance and reduce power consumption.
problem Training neural networks with high Bit Error Rates (BERs).
method Trained three Binarized Convolutional Neural Network architectures on various datasets with high BERs.
result High BERs do not significantly degrade test accuracy, enabling more efficient hardware.
Study examines how liquidity constraints impact optimal retirement decisions.
problem Impact of liquidity constraints on optimal retirement decisions.
method Analytical solution using duality method with different liquidity constraints.
result Sensitivity analysis reveals the effect of liquidity constraints on retirement decisions.
Paper proposes a neural network for disaggregating appliance-level energy consumption.
problem Estimating appliance-level electricity consumption from a single meter.
method Adapts a neural network to classify operational state changes of appliances.
result Competitive performance compared to existing methods in simulated experiments.
Optimal scheduling of hydrogen production in dynamic pricing power market can maximize the profit of hydrogen producer; however, it highly depends on the accurate forecast of hydrogen consumption. In this paper, we propose a deep leaning based forecasting approach for predicting hydrogen consumption of fuel cell vehicl…
We study an optimal consumption and investment problem in a possibly incomplete market with general, not necessarily convex, stochastic constraints. We give explicit solutions for investors with exponential, logarithmic and power utility. Our approach is based on martingale methods which rely on recent results on the e…
Investor optimizes investment and consumption under uncertain market conditions with constraints.
problem Investor optimizes investment and consumption in a stochastic environment with model uncertainty and constraints.
method Robust control problem solved using stochastic Hamilton-Jacobi-Bellman-Isaacs equations, backward stochastic differential equations, and bounded mean oscillation martingale theory.
result Investor incurs utility loss when ignoring model uncertainty, and constraints impact optimal strategy and value function.
The paper benchmarks data stream classifiers for human activity recognition on connected devices.
problem Challenges in human activity recognition on connected devices, particularly high memory consumption and low F1 scores.
method Evaluation of five stream classification algorithms on real and synthetic datasets, measuring both performance and resource consumption.
result HT and MF classifiers show superior performance and resilience to concept drift compared to other algorithms.
We investigate optimal consumption problems for a Black-Scholes market under uniform restrictions on Value-at-Risk and Expected Shortfall for logarithmic utility functions. We find the solutions in terms of a dynamic strategy in explicit form, which can be compared and interpreted. This paper continues our previous wor…
Paper uses tensor completion to estimate HVAC fan power baselines.
problem Estimating HVAC fan power without demand response.
method Tensor completion for multi-dimensional data analysis.
result Tensor completion outperforms existing baselining methods.
Study Nash equilibria in mean field portfolio games with consumption.
problem Finding Nash equilibria in mean field portfolio games with consumption.
method Established a correspondence between equilibria and solutions to FBSDEs, using martingale and dynamic programming principles.
result Proved the uniqueness of Nash equilibrium in closed form under certain conditions.
Distribution grid is the medium and low voltage part of a large power system. Structurally, the majority of distribution networks operate radially, such that energized lines form a collection of trees, i.e. forest, with a substation being at the root of any tree. The operational topology/forest may change from time to …
A brief overview of the models and data analyses of income, wealth, consumption distributions by the physicists, are presented here. It has been found empirically that the distributions of income and wealth possess fairly robust features, like the bulk of both the income and wealth distributions seem to reasonably fit …
We discuss an optimal investment, consumption and insurance problem of a wage earner under inflation. Assume a wage earner investing in a real money account and three asset prices, namely: a real zero coupon bond, the inflation-linked real money account and a risky share described by jump-diffusion processes. Using the…
Paper applies FloatSD8 to LSTM networks, reducing complexity and power.
problem Training and inference complexity of LSTM networks.
method Applied FloatSD8 for weights, 8-bit quantization for gradients/activations, reduced arithmetic precision.
result Successfully trained LSTM models with reduced complexity and preserved accuracy.
Study optimal investment and consumption strategies for competitive agents with habit formation.
problem Optimal investment and consumption strategies for competitive agents with habit formation.
method Formulated n-agent game problems and mean field game problems, derived mean field equilibrium, constructed approximate Nash equilibrium.
result Explicit convergence order of approximate Nash equilibrium can be obtained.
Enhances load forecasting for multiple entities with dynamic similarities.
problem Inaccurate probabilistic load predictions due to uncertainties and dynamic changes.
method Online multi-task learning for probabilistic load forecasting.
result Significantly enhances load forecasting accuracy across various scenarios.
ONLAD Core detects anomalies in edge devices with fast learning and low power.
problem Anomaly detection in edge devices with concept drift and data transfers.
method Highly optimized neural network-based anomaly detection on edge devices.
result ONLAD Core achieves fast anomaly detection and low power consumption.
In this paper, we investigate an optimal investment and consumption problem for an investor who trades in a Black--Scholes financial market with stochastic coefficients driven by a non-Gaussian Ornstein--Uhlenbeck process. We assume that an agent makes investment and consumption decisions based on a power utility funct…
Study Epstein-Zin preferences in mean field portfolio games, proving unique equilibria.
problem Analyzing portfolio games with Epstein-Zin preferences under non-Markovian conditions.
method Proves a one-to-one correspondence between Nash equilibria and BSDE solutions, using local stochastic maximum principle tailored to Epstein-Zin utility.
result Establishes uniqueness of equilibria in mean field portfolio games under Epstein-Zin preferences.
Paper proposes MAMRL for efficient energy dispatch in self-powered edge computing systems.
problem High energy consumption in self-powered edge computing systems.
method Developed a semi-distributed data-driven MAMRL framework to solve a two-stage linear stochastic programming problem.
result The proposed MAMRL framework reduces up to 11% non-renewable energy usage and 22.4% energy cost.
We investigate optimal consumption and investment problems for a Black-Scholes market under uniform restrictions on Value-at-Risk and Expected Shortfall. We formulate various utility maximization problems, which can be solved explicitly. We compare the optimal solutions in form of optimal value, optimal control and opt…
Hyperflux models pruning as a system to reveal weight importance.
problem Pruning large neural networks to reduce latency and power consumption.
method Introduces Hyperflux, a novel L0 method that models pruning as flux and pressure. result Achieves competitive results with ResNet-50, VGG-19, and DeiT-T/S on various datasets.
In a continuous time stochastic economy, this paper considers the problem of consumption and investment in a financial market in which the representative investor exhibits a change in the discount rate. The investment opportunities are a stock and a riskless account. The market coefficients and discount factor switches…
New method tracks electricity flows to measure carbon emissions in real-time.
problem Accurate measurement of carbon emissions from electricity consumption.
method Real-time consumption-based accounting using flow tracing.
result Substantial differences found between production and consumption intensities.
We study optimal investment strategies that maximize expected utility from consumption and terminal wealth in a pure-jump asset price model with Markov-modulated (regime switching) jump-size distributions. We give sufficient conditions for existence of optimal policies and find closed-form expressions for the optimal v…
PSQRNN model forecasts electricity consumption in China by integrating neural networks and quantile regression.
problem Electricity forecasting in China due to regional economic, social, and natural conditions.
method PSQRNN combines neural networks and semiparametric quantile regression to model electricity consumption.
result PSQRNN model outperforms traditional methods in forecasting electricity consumption in China.