This research focuses on medium-term load forecasting in a tropical region post-pandemic. This study presents one of the first attempts to analyze medium-term forecasting using half-hourly resolution in the Java-Bali power system post-COVID-19 period...
Over the last decades, load forecasting is used by power companies to balance energy demand and supply. Among the several load forecasting methods, medium-term load forecasting is necessary for grid’s maintenance planning, settings of electrici...
Time series analysis using long short term memory (LSTM) deep learning is a very attractive strategy to achieve accurate electric load forecasting. Although it outperforms most machine learning approaches, the LSTM forecasting model still reveals a l...
Accurate medium- and long-term power load forecasting is of great significance for the scientific planning and safe operation of power systems. Monthly power load has multiscale time series correlation and seasonality. The existing models face the pr...
In urban power planning, it is typically necessary to predict future monthly, quarterly, and annual electricity consumption to conduct advance planning and ensure the stable operation of the power grid. Therefore, accurate medium-term load forecastin...
Accurate medium- and long-term electricity peak load forecasting is critical for power system operation, planning, and electricity trading. However, peak load forecasting is challenging because of the complex and nonlinear relationship between peak l...
In the energy-planning sector, the precise prediction of electrical load is a critical matter for the functional operation of power systems and the efficient management of markets. Numerous forecasting platforms have been proposed in the literature t...
Due to severe climate change impact on electricity consumption, as well as new trends in smart grids (such as the use of renewable resources and the advent of prosumers and energy commons), medium-term and long-term electricity load forecasting has b...
This work proposes a quantile regression neural network based on a novel constrained weighted quantile loss (CWQLoss) and its application to probabilistic short and medium-term electric-load forecasting of special interest for smart grids operations....
Background: With the development of smart grids, accurate electric load forecasting has become increasingly important as it can help power companies in better load scheduling and reduce excessive electricity production. However, developing and select...
Accurate and reliable medium- and long-term load forecasting is crucial for the rational planning and operation of power systems. However, existing methods often struggle to accurately extract and capture long-term dependencies in load data, leading...
In this work we propose a new hybrid model, a combination of the manifold learning Principal Components (PC) technique and the traditional multiple regression (PC-regression), for short and medium-term forecasting of daily, aggregated, day-ahead, ele...
This article provides a solution based on statistical methods (ARIMA, ETS, and Prophet) to predict monthly power demand, which approximates the relationship between historical and future demand patterns. The energy demand time series shows seasonal f...
Nowadays, electric load forecasting through a data analytic approach has become one of the most active and emerging research areas. It provides future consumption patterns of electric load. Since there are large fluctuations in both electricity produ...
Electrical load forecasting provides knowledge about future consumption and generation of electricity. There is a high level of fluctuation behavior between energy generation and consumption. Sometimes, the energy demand of the consumer becomes highe...
Accurate forecasting of power plant loads is critical for maintaining a stable power supply, minimizing grid fluctuations, and enhancing power market trading mechanisms. However, the data on power plant generation load (hereinafter abbreviated as loa...
This research focuses its efforts on the prediction of medium-term electricity consumption for scenarios of highly variable electricity demand. Numerous approaches are used to predict electricity demand, among which the use of time series (ARMA, ARIM...
Accurate power load forecasting is critical to achieving the sustainability of energy management systems. However, conventional prediction methods suffer from low precision and stability because of crude modules for predicting short-term and medium-t...
The main objective of this study was to conduct multi-stage and multi-variant prognostic research to assess the impact of e-mobility development on the Polish power system for the period 2022–2027. The research steps were as follows: forecast t...
Forecasting domestic and foreign power demand is crucial for planning the operation and expansion of facilities. Power demand patterns are very complex owing to energy market deregulation. Therefore, developing an appropriate power forecasting model...
Accurate load forecasting is essential for the efficient and reliable operation of power systems. Traditional models primarily utilize unidirectional data reading, capturing dependencies from past to future. This paper proposes a novel approach that...
Electricity load prediction is an essential tool for power system planning, operation and management. The critical information it provides can be used by energy providers to maximise power system operation efficiency and minimise system operation cos...
Forecasting energy demand within a distribution network is essential for developing strategies to manage and optimize available energy resources and the associated infrastructure. In this study, we consider remote communities in the Arctic located at...
The realization of load forecasting studies within the scope of forecasting periods varies depending on the application areas and estimation purposes. It is mainly carried out at three intervals: short-term, medium-term, and long-term. Short-term loa...
This paper introduces a novel weighted fusion methodology for grid-level short-term load forecasting that addresses the critical limitations of direct aggregation methods currently used by regional dispatch centers. Traditional approaches accumulate...
Determining/predicting the environment dominates a variety of marine operations, such as route planning and offshore installation. Significant wave height (Hs) is a critical parameter-defining wave, a dominating marine load. Data-driven machine learn...
This paper seeks to identify bottlenecks in the energy grid supply regarding different market penetration of battery electric vehicles in Stuttgart, Germany. First, medium-term forecasts of electric and hybrid vehicles and the corresponding charging...
With the globally increasing electricity demand, its related uncertainties are on the rise as well. Therefore, a deeper insight of load forecasting techniques for projecting future electricity demands becomes imperative for business entities and poli...
Short-term power load forecasting at the regional level is essential for maintaining grid stability and optimizing power generation, consumption, and maintenance scheduling. Considering the temporal, periodic, and nonlinear characteristics of power l...
Medium-term electricity consumption and load forecasting in smart grids is an attractive topic of study, especially using innovative data analysis approaches for future energy consumption trends. Loss of electricity during generation and use is also...
Medium-and-long-term load forecasting plays an important role in energy policy implementation and electric department investment decision. Aiming to improve the robustness and accuracy of annual electric load forecasting, a robust weighted combinatio...
Short-to-medium-term electric load forecasting is crucial for grid planning, transformation, and load scheduling for power supply departments. Various complex and ever-changing factors such as weather, seasons, regional economic structures, and enter...
Temperature is widely known as one of the most important drivers to forecast electricity and gas variables, such as the load. Because of that reason, temperature forecasting is and has been for years of great interest for energy forecasters and sever...
The traditional power load forecasting learning method has problems such as overfitting and incomplete learning of time series information when dealing with complex nonlinear data, which affects the accuracy of short–medium term power load fore...
In the burgeoning field of sustainable energy, this research introduces a novel approach to accurate medium- and long-term load forecasting in large-scale power systems, a critical component for optimizing energy distribution and reducing environment...
This article presents a comprehensive review of methods used for forecasting electricity consumption. The studies analyzed by the authors encompass both classical statistical models and modern approaches based on artificial intelligence, including ma...
The rapid proliferation of electric vehicles necessitates accurate forecasting of charging pile capacity for urban power system planning, yet existing methods for medium- to long-term prediction lack effective mechanisms to capture complex multi-fact...
This study seeks to improve the accuracy of air conditioning load forecasting to address the challenges of load management in power systems during high-temperature periods in the summer. Given the limitations of traditional forecasting models in capt...
In the current trend of consumption, electricity consumption will become a very high cost for the end-users. Consumers acquire energy from suppliers who use short, medium, and long-term forecasts to place bids in the power market. This study offers a...
This paper examines how Australian residential load profiles may evolve in the short to medium term future. These profiles can be used to support simulation studies of the future Australian network within an environment that is transitioning to renew...
This research presents an advanced methodology for smart management of energy losses in electrical distribution networks by leveraging deep neural network architectures. The primary objective is to enhance the accuracy of short-term forecasting for n...
Based on wind lidar, a novel yaw control scheme was designed that utilizes forecast wind information. The new scheme can reduce the power loss caused by the lag of accurate measurement data in the traditional yaw control strategy. A theoretical analy...
The increasing penetration of renewable energy sources tends to redirect the power systems community’s interest from the traditional power grid model towards the smart grid framework. During this transition, load forecasting for various time ho...
The atmospheric molecular number density can be obtained from atmospheric temperature and pressure profiles and is a significant input parameter for the inversion of lidar measurements. When measurements of vertical profiles of temperature and pressu...
The dissemination of low-carbon technologies, such as urban photovoltaic distributed generation, imposes new challenges to the operation of distribution grids. Distributed generation may introduce significant net-load asymmetries between feeders in t...
10 December 2021
A strong heat load in buildings and cities during the summer is not a new phenomenon. However, prolonged heat waves and increasing urbanization are intensifying the heat island effect in our cities; hence, the heat exposure in residential buildings....
Distribution networks are undergoing radical changes due to the high level of penetration of dispersed generation. Dispersed generation systems require particular attention due to their incorporation of uncertain energy sources, such as wind farms, a...
Accurate load profile identification is crucial for effective and sustainable power system planning. This study proposes a characterization methodology based on clustering techniques applied to consumption data from medium- and low-voltage users, as...
The response of low-level cloud properties to aerosol loading remains ambiguous, particularly due to the confounding influence of meteorological factors and water vapor availability. We utilize long-term data from Ka-band Zenith Radar, Clouds and the...
Urban boundary layer characterization is currently a challenging and relevant issue, because of its role in weather and air quality modelling and forecast. In many cities, the effect of complex topography at local scale makes this modelling even more...