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.
Paper proposes dense average network for improved power load forecasting.
Paper proposes a new method for hourly load forecasting using smart meter data.
We propose a new forecasting method for predicting load demand and generation scheduling. Accurate week-long forecasting of load demand and optimal power generation is critical for efficient operation of power grid systems. In this work, we use a synthetic data set describing a power grid with 700 buses and 134 generat…
Paper uses econometrics time series model with T-student Distribution for short-term load forecasting.
Adaptive probabilistic load forecasting improves performance in power systems.
Paper benchmarks and customizes energy forecasting methods.
Deep learning boosts building energy load forecasting.
Short-term load forecasting is a critical element of power systems energy management systems. In recent years, probabilistic load forecasting (PLF) has gained increased attention for its ability to provide uncertainty information that helps to improve the reliability and economics of system operation performances. This…
An accurate load forecasting has always been one of the main indispensable parts in the operation and planning of power systems. Among different time horizons of forecasting, while short-term load forecasting (STLF) and long-term load forecasting (LTLF) have respectively got benefits of accurate predictors and probabil…
Enhances load forecasting for multiple entities with dynamic similarities.
Model uses GAMs to forecast hourly electricity load weeks to one year ahead.
Short-term load forecasting (STLF) is essential for the reliable and economic operation of power systems. Though many STLF methods were proposed over the past decades, most of them focused on loads at high aggregation levels only. Thus, low-aggregation load forecast still requires further research and development. Comp…
This paper uses a diffusion model to forecast electrical loads with uncertainty.
We present in this paper a model for forecasting short-term power loads based on deep residual networks. The proposed model is able to integrate domain knowledge and researchers' understanding of the task by virtue of different neural network building blocks. Specifically, a modified deep residual network is formulated…
Management and efficient operations in critical infrastructure such as Smart Grids take huge advantage of accurate power load forecasting which, due to its nonlinear nature, remains a challenging task. Recently, deep learning has emerged in the machine learning field achieving impressive performance in a vast range of …
Model forecasts hourly electricity demand influenced by weather, socio-economic, and political factors.
Power load forecast with Machine Learning is a fairly mature application of artificial intelligence and it is indispensable in operation, control and planning. Data selection techniqies have been hardly used in this application. However, the use of such techniques could be beneficial provided the assumption that the da…
Hybrid model combines LSTM and ETS for mid-term electric load forecasting.
With the growing prevalence of smart grid technology, short-term load forecasting (STLF) becomes particularly important in power system operations. There is a large collection of methods developed for STLF, but selecting a suitable method under varying conditions is still challenging. This paper develops a novel reinfo…
Paper presents a method for probabilistic load forecasting using adaptive online learning.
An increase in energy production from renewable energy sources is viewed as a crucial achievement in most industrialized countries. The higher variability of power production via renewables leads to a rise in ancillary service costs over the power system, in particular costs within the electricity balancing markets, ma…
Simple 1D-CNN network predicts electricity loads 36 hours ahead.
The paper proposes a new method for probabilistic load forecasting using Bernstein-Polynomial Normalizing Flows.
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 …
Paper proposes a method for weather-informed probabilistic forecasting and scenario generation in power systems.
Deep learning improves weather modeling for electricity load forecasting.
We present a methodology for probabilistic load forecasting that is based on lasso (least absolute shrinkage and selection operator) estimation. The model considered can be regarded as a bivariate time-varying threshold autoregressive(AR) process for the hourly electric load and temperature. The joint modeling approach…
The study improves load forecasting for electricity consumers using advanced machine learning models.
Bayesian Transformer improves probabilistic load forecasting with calibrated uncertainty estimates.
Paper proposes combining GAM and DNN for accurate peak demand estimation from lower-resolution data.
Paper introduces normalizing flows for accurate probabilistic energy forecasting.
We present a simple quantile regression-based forecasting method that was applied in a probabilistic load forecasting framework of the Global Energy Forecasting Competition 2017 (GEFCom2017). The hourly load data is log transformed and split into a long-term trend component and a remainder term. The key forecasting ele…
The key contribution of this paper is to propose a classification into two dimensions of the load forecasting studies to decide which forecasting tools to use in which case. This classification aims to provide a synthetic view of the relevant forecasting techniques and methodologies by forecasting problem. In addition,…
New algorithm extracts device profiles for short-term power predictions in commercial buildings.
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…
This paper monetizes customer load data to boost energy retailer profits.
Active learning reduces smart meter data needs for better electric load predictions.
New methods improve electricity load forecasting using hierarchical transfer learning.
Improved electrical load forecasting model using Fourier-enhanced RNN.
Paper optimizes demand aggregation for low-level electricity markets.
Contemporary power grids are being challenged by rapid voltage fluctuations that are caused by large-scale deployment of renewable generation, electric vehicles, and demand response programs. In this context, monitoring the grid's operating conditions in real time becomes increasingly critical. With the emergent large …
Frugal method predicts multiple local electricity loads efficiently.
A hybrid strategy forecasts short-term loads using Warm-start Gradient Tree Boosting.
This paper reviews low voltage load forecasting methods and applications.
The increasing importance of renewable energy, especially solar and wind power, has led to new forces in the formation of electricity prices. Hence, this paper introduces an econometric model for the hourly time series of electricity prices of the European Power Exchange (EPEX) which incorporates specific features like…
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…
Paper assesses the market value of sharing privacy-protected smart meter data.
Single layer Feedforward Neural Network(FNN) is used many a time as a last layer in models such as seq2seq or could be a simple RNN network. The importance of such layer is to transform the output to our required dimensions. When it comes to weights and biases initialization, there is no such specific technique that co…