Probabilistic modeling of net load forecast errors is an important approach for reserve decision-making in power systems with a high penetration of renewable energy. However, existing probabilistic modeling methods face issues such as insufficient es...
Photovoltaic (PV) power generation has brought about enormous economic and environmental benefits, promoting sustainable development. However, due to the intermittency and volatility of PV power, the high penetration rate of PV power generation may p...
Accurate photovoltaic power probability forecasting assists dispatch departments in making rational decisions. Joint probability distributions constructed using Copula functions can flexibly characterize complex nonlinear correlations and tail depend...
Interval forecasting has become a research hotspot in recent years because it provides richer uncertainty information on wind power output than spot forecasting. However, compared with studies on single wind farms, fewer studies exist for multiple wi...
In comparison with traditional point forecasting method, probability density forecasting can reflect the load fluctuation more effectively and provides more information. This paper proposes a hybrid hourly power load forecasting model, which integrat...
Wind speed forecasting is advantageous in reducing wind-induced accidents or disasters and increasing the capture of wind power. Accordingly, this forecasting process has been a focus of research in the field of engineering. However, because wind spe...
In the current energy context of intelligent buildings and smart grids, the use of load forecasting to predict future building energy performance is becoming increasingly relevant. The prediction accuracy is directly influenced by input uncertainties...
Time series forecasting is a challenging problem in the field of data mining. Deterministic forecasting has shown limitations in the field. Therefore, researchers are now more inclined towards probabilistic forecasting, which has shown a clear advant...
Photovoltaic power generation has high variability and uncertainty because it is affected by uncertain factors such as weather conditions. Therefore, probabilistic forecasting is useful for optimal operation and risk hedging in power systems with lar...
Static forecast models used by gas distribution operators in regulatory submissions provide point estimates but cannot quantify the probability that approved targets will be met. This paper proposes an Extended Kalman Filter–Monte Carlo (EKF&nd...
Solar energy generated from PhotoVoltaic (PV) systems is one of the most promising types of renewable energy. However, it is highly variable as it depends on the solar irradiance and other meteorological factors. This variability creates difficulties...
Based on 15-min high-frequency power load data from a Chinese hospital, by adopting recurrence interval analysis, an attempt is made to provide a new perspective for improving hospital energy administration in electrical efficiency and safety. Initia...
Photovoltaic (PV) power interval forecasting models are commonly trained on data dominated by non-ramp samples, which may weaken uncertainty characterization during rapid power changes. This study proposes a ramp-aware quantile regression Temporal Fu...
The intermittency of renewable energy will increase the uncertainty of the power system, so it is necessary to predict the short-term wind power, after which the electrical power system can operate reliably and safely. Unlike the traditional point fo...
Accurate ultra-short-term power interval forecasting for distributed photovoltaic (DPV) systems is crucial for the intra-day operation of distribution networks. However, existing studies still face two major limitations. First, most spatiotemporal fo...
Earthquakes cause enormous social and economic damage. Consequently, the seismic process requires regular monitoring and systematic forecasting of strong earthquakes. This study introduces an enhanced iteration of the method of the minimum area of al...
Electricity load-forecasting is an essential tool for effective power grid operation and energy markets. However, the lack of accuracy on the estimation of the electricity demand may cause an excessive or insufficient supply which can produce instabi...
Effective and reliable load forecasting is an important basis for power system planning and operation decisions. Its forecasting accuracy directly affects the safety and economy of the operation of the power system. However, attaining the desired poi...
Modern power transmission grids are increasingly operated under volatile load, variable generation, and near-limit line-flow conditions. In such environments, deterministic line-flow forecasting is insufficient for operational decision support becaus...
In order to enhance the accuracy of short-term wind power forecasting (WPF), a short-term wind power forecasting method based on historical wind resources by data mining has been designed. Firstly, the spoiled data resulting from wind turbine and met...
With the levels of confidence and system complexity, interval forecasts and entropy analysis can deliver more information than point forecasts. In this paper, we take receivers’ demands as our starting point, use the trade-off model between accuracy...
The integration of large-scale wind power into the power grid threatens the stable operation of the power system. Traditional wind power prediction is based on time series without considering the variability between wind turbines in different locatio...
Due to its variability, solar power generation poses challenges to grid energy management. In order to ensure an economic operation of a national grid, including its stability, it is important to have accurate forecasts of solar power. The current pa...
The design and operation of reservoirs based on conventional flood-limited water levels (FLWL) implicitly adopts the assumption of hydrological stationarity. As such, historical-record-based FLWL may not be the best choice for flood-control operation...
Short-term hourly load forecasting in South Africa using additive quantile regression (AQR) models is discussed in this study. The modelling approach allows for easy interpretability and accounting for residual autocorrelation in the joint modelling...
We develop a new probabilistic forecasting method for global horizontal irradiation (GHI) by extending our previous bootstrap method to a case of an exponentially decaying heteroscedastic model for tracking dynamics in solar radiance. Our previous me...
Wind power interval forecasting remains challenging due to the uncertainty and strong variability of wind generation. To capture temporal dependency and predictive uncertainty, this paper proposes a hybrid interval forecasting framework that integrat...
Accurate and trustworthy forecasting of coal prices can offer theoretical support for the rational planning of coal industry output, which is of great importance in ensuring a stable and sustainable energy supply and in achieving carbon neutrality ta...
Accurate forecasting of photovoltaic (PV) power is of great significance for the safe, stable, and economical operation of power grids. Therefore, a day-ahead photovoltaic power forecasting (PPF) and uncertainty analysis method based on WT-CNN-BiLSTM...
A comprehensive and accurate wind power forecast assists in reducing the operational risk of wind power generation, improves the safety and stability of the power system, and maintains the balance of wind power generation. Herein, a hybrid wind power...
Possible risks in reservoir flood control and regulation cannot be objectively assessed by deterministic flood forecasts, resulting in the probability of reservoir failure. We demonstrated a risk analysis of reservoir flood routing calculation accoun...
Natural gas is one of the most important sources of energy in modern society. However, its strong volatility highlights the importance of accurately forecasting natural gas price trends and movements. The nonlinear nature of the natural gas price ser...
In this paper, the performances of two approaches for solar probabilistic are evaluated using a set of metrics previously tested by the meteorology verification community. A particular focus is put on several scores and the decomposition of a specifi...
To face the growing electricity demand, several countries have adopted the solution of clean energy and use renewable energy sources (e.g., wind and solar) to reinforce the stability of the power network, especially during peak demand periods. Foreca...
This study audits whether regime weighting can sharpen conformal forecast intervals without using target-period information or omitting the required finite-sample correction. Conformal prediction builds such intervals from past forecast errors. A nat...
The Meteo-France seasonal forecasting system 7 provides a 7-month forecast range with 25 ensembles. The seasonal precipitation re-forecast (from May to November 1993–2015) was evaluated by the Brier score in terms of accuracy and reliability ba...
Highly convection-related short-duration heavy rainfall (SDHR), defined as rainfall greater than 20 mm h−1 of a whole hour, causes severe damage every year in China. An objective forecasting method is developed to provide guidance products for...
Many time series forecasting applications use ranges rather than point forecasts. Producing forecasts in the form of Prediction Intervals (PIs) is natural, since intervals are an important component of many mathematical models. The LUBE (Lower Upper...
Accurate regional demand forecasting supports reliable operation of urban electric vehicle (EV) charging networks. However, public charging demand exhibits spatial heterogeneity, multi-scale periodicity, and uncertainty. This paper proposes Urban-CST...
Short-term load forecasting is an important part of load forecasting, which is of great significance to the optimal power flow and power supply guarantee of the power system. In this paper, we proposed the load series reconstruction method combined i...
With the increasing penetration of renewable energy into power systems, wind and photovoltaic (PV) power generation exhibit substantial uncertainty due to meteorological variability, which becomes more pronounced under extreme weather conditions. The...
The adjoining parts of Northeast India represent one of the most seismically active regions of the Indian subcontinent. This region is classified as Zone V by the Indian Seismic Code, indicating maximum seismic hazard potential. This region has a his...
The occurrence of fog often causes errors in the prediction of the incident solar radiation and the power produced by photovoltaic cells. An accurate fog forecast would benefit solar energy producers and grid operators, who could take coordinated act...
We examine a causally specified geometric reference for forecasting a two-sided path event: whether the absolute cumulative log return from the forecast origin reaches 0.05 at any of the next 20 trading observations. The Geometric Curve Manifold (GCM...
Under high renewable energy penetration, wind power forecasting faces pronounced challenges due to strong randomness and uncertainty, making conventional point-forecast-centric paradigms insufficient for risk-aware and reliable power system schedulin...
The core component to ensure the refined and safe operation of distribution network scheduling is 10 kV bus load probabilistic prediction. However, existing probabilistic prediction methods suffer from insufficient dynamic feature extraction and comp...
In recent years, the complexity and suddenness of infectious disease transmission have posed significant limitations for traditional time-series forecasting methods when dealing with the nonlinearity, non-stationarity, and multi-peak distributions of...
Solar power’s variability makes managing power system planning and operation difficult. Facilitating a high level of integration of solar power resources into a grid requires maintaining the fundamental power system so that it is stable when in...
High renewable penetration makes day-ahead operation sensitive to the directional effects of wind and photovoltaic forecast errors. Conventional prediction intervals mainly evaluate coverage and sharpness, but lower- and upper-boundary violations cor...
The inherent nonlinearity and uncertainty of wind power generation pose significant challenges to the security, stability, and economic operation of power grids. Therefore, accurate and reliable wind power forecasting is crucial for seamless grid int...
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