Load Forecasting - Science topic

In power systems, voltage collapse during overload can be a significant threat. Accurate forecasting of critical operational conditions within power grids is crucial for preventing such situations. Precise predictions of voltage collapse enable operators to monitor the system closely and implement necessary corrective measures promptly, avoiding po...

As the complexity of power systems increases, accurate load forecasting becomes crucial. This paper proposes a method for short-term electrical load forecasting that integrates fuzzy rough set (FRS) theory and multi-kernel extreme learning machine (MKELM) to improve both the accuracy and reliability of load predictions. First, we introduce the FRS...

As the electricity landscape undergoes transformative shifts with the rising prevalence of photovoltaic (PV) systems and the growing stochastic nature of electricity consumption from electric vehicles (EVs), the significance of accurate probabilistic forecasting becomes paramount. This paper explores and evaluates key metrics employed in assessing...

Accurate short-term forecasting of power load is essential for the reliable operation of the comprehensive energy systems of ports and for effectively reducing energy consumption. Owing to the complexity of port systems, traditional load forecasting methods often struggle to capture the non-linearity and multifactorial interactions within the facto...

Short-term load forecasting plays a crucial role in managing the energy consumption of buildings in cities. Accurate forecasting enables residents to reduce energy waste and facilitates timely decision-making for power companies’ energy management. In this paper, we propose a novel hybrid forecasting model designed to predict load series in multipl...

Renewable energy sources are essential in fulfilling the increasing need for electricity. Researchers are actively exploring eco-friendly alternative energy sources and technologies, particularly in the form of micro grids or gridintegrated systems. A hybrid renewable energy system with battery storage in a small-scale industry was optimized using...

With the continuous development of smart grid construction and the gradual improvement of power market operation mechanisms, the importance of power load forecasting is continually increasing. In this study, a short-term load prediction method based on the fuzzy optimization combined model of load feature recognition was designed to address the pro...

The high penetration of distributed energy resources poses significant challenges to the dispatch and operation of power systems. Improving the accuracy of short-term load forecasting (STLF) can optimize grid management, leading to increased economic and social benefits. Currently, some simple AI and hybrid models has issues to deal with and strugg...

Predictive Analytics is the process of using prediction active modelling techniques to analyse the various types of data in agriculture, business management, engineering, weather forecasting, planning, meteorology etc. Most frequently using predictive modelling technique in Electrical load forecasting and Agricultural production forecasting is the...

Short-term power load forecasting is the basis for ensuring the safe and stable operation of the power system. However, because power load forecasting is affected by weather, economy, geography, and other factors, it has strong instability and nonlinearity, making it difficult to improve the accuracy of short-term power load forecasting. To solve t...

Accurate forecasting of electric vehicle (EV) load is essential for grid stability and energy management. EV load forecasting is influenced by multiple factors. At present, the load forecasting model for EVs mainly uses collected sample data to build a data‐driven model. But these algorithms need to collect all the data together to train the model,...

In this study, we delve into the realm of meta-learning to combine point base forecasts for probabilistic short-term electricity demand forecasting. Our approach encompasses the utilization of quantile linear regression, quantile regression forest, and post-processing techniques involving residual simulation to generate quantile forecasts. Furtherm...

The accurate short-term point and probabilistic load forecasts are critically important for efficient operation of power systems and electricity bargain in the market. Fuzzy systems achieved limited success in electric load forecasting. On the other hand, support vector regression models have seldom been part of a winning solution of the electric l...

The proliferation of electric vehicles (EVs) necessitates accurate EV charging load forecasting for demand-side management and electric-grid planning. Conventional machine learning-based load forecasting methods like long short-term memory (LSTM) neural networks rely on large amounts of historical data, which can be resource-intensive and time-cons...

Accurate power load forecasting is crucial for the sustainable operation of smart grids. However, the complexity and uncertainty of load, along with the large-scale and high-dimensional energy information, present challenges in handling intricate dynamic features and long-term dependencies. This paper proposes a computational approach to address th...

The energy sector heavily relies on a diverse array of machine learning algorithms for power load prediction, which plays a pivotal role in shaping policies for power generation and distribution. The precision of power load prediction depends on numerous factors that reflect nonlinear traits within the data. Notably, machine learning algorithms and...

Long Short-Term Memory Recurrent Neural Networks (LSTM) are used to build surrogate models to forecast time-series blade loads for both fixed and floating offshore wind turbines. In this paper, we train surrogate models on datasets generated with OpenFAST on the IEA-15MW-RWT under a range of metocean conditions. The aim of the surrogate models is t...

: Leveraging the sustainability of the power system market, researchers have developed various ML models for forecasting electricity demand. The LSSVM is well suited to handle complex non-linear power load series. However, the less optimal regularization parameter and the Gaussian kernel function in the LSSVM model have contributed to flawed foreca...

Accurate power load forecasting is critical to maintaining the stability and efficiency of power systems. However, due to the complex and fluctuating nature of power load patterns, physical calculations are often inefficient and time-consuming. In addition, traditional methods, known as statistical learning methods, require not only mathematical ba...

In this paper, comparison of machine learning models for short-term forecasting of distribution station feeders load is presented. Specifically, load profile datasets from four different feeders in a power distribution station located in Akwa Ibom State Nigeria are used to train the two different machine learning models, namely, the recurrent neura...

The precise forecasting of electricity demand also referred to as load forecasting, is essential for both planning and managing a power system. It is crucial for many tasks, including choosing which power units to commit to, making plans for future power generation capacity, enhancing the power network, and controlling electricity consumption. As B...

With the increasing a huge amount of end users using electricity in modern cities, smart grids have some critical problems for energy efficiency and managing renewable energy resources. Therefore, electricity load forecasting is an important strategy to avoid power disconnection and power communication damages in smart grids. On the other hand, the...

Accurate and interpretable short-term load forecasting tasks are essential to the optimal operation of liberalized electricity markets since they contribute to the efficient development of energy trading and demand response strategies as well as the successful integration of renewable energy sources. Consequently, performant day-ahead consumption f...

The charging behavior of electric vehicle users is highly stochastic, which makes the short-term prediction of charging load at electric vehicle charging stations difficult. In this paper, a data-driven hybrid model optimized by the improved dung beetle optimization algorithm (IDBO) is proposed to address the problem of the low accuracy of short-te...

In the dynamic smart grid landscape, accurate probabilistic forecasting of electric load is critical. This paper presents a novel 24-hour-ahead probabilistic load forecasting model by integrating quantile regression with a parallel convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) architecture. Carefully tuning hyper...

There are several complex and unpredictable aspects that affect the power grid. To make short-term power load forecasting more accurate, a short-term power load forecasting model that utilizes the VMD-Crossformer is suggested in this paper. First, the ideal number of decomposition layers was ascertained using a variational mode decomposition (VMD)...

Microgrids driven by distributed energy resources are gaining prominence as decentralized power systems offering advantages in energy sustainability and resilience. However, optimizing microgrid operation faces challenges from the intermittent nature of renewable sources, dynamic energy demand, and varying grid electricity prices. This paper presen...

In today's world, where economic and industrial development continues, the importance of electrical energy is constantly increasing. Energy demand should be forecast as precisely as possible to reduce lost energy costs in the system, to plan generation expenditures appropriately, to ensure that market players are not economically harmed, and to del...

It is very important to optimize the power supply and alleviate the conflict of electric load demand, so an accurate and reasonable power load prediction technology has been the focus of research. An evolutional LSTM named Bi-LSTM processes two independent input sequences with two opposite time directions, which aims to improve the efficiency of lo...

This study proposes an innovative method for forecasting electricity load that combines NeuralProphet’s time series analysis capability with Bi-LSTM-SA’s self-attention mechanism. The method improves prediction accuracy, reliability, and interpretability by analyzing trends, cycles, and holiday impacts, as well as considering climatic factors as ke...

The operational stability of a power transformer exerts an extremely important impact on the power symmetry, balance, and security of power systems. When the grid load fluctuates greatly, if the load factor of the transformer cannot be maintained within a reasonable range, it leads to increased instability in grid operation. Adjusting the transform...

In the context of power saving, the increasing energy consumption of air conditioning and the limited energy demand has become an urgent problem. Therefore, according to the feature that the accuracy of grid load forecasting improves gradually with the shortening of time scale and the flexible intra-day regulation characteristics of heat pump units...

Electricity consumption is expected to increase considerably in the next few years, so forecasting and planning will become more important. A new method of forecasting electricity loads based on air pollution is presented in this paper. Air pollution indirect effects are not incorporated in current evaluations since they rely primarily on weather c...

Accurate short-term load forecasting (STLF) plays an essential role in sustainable energy development. Specifically, energy companies can efficiently plan and manage their generation capacity, lessening resource wastage and promoting the overall efficiency of power resource utilization. However, existing models cannot accurately capture the nonline...

In polar ship hull structural designs, methods based on regulations are considered the most authoritative; however, they tend to be conservative and often exhibit a notable degree of redundancy. This study aims to evaluate the applicability of the empirical formula for ice load assessments by conducting a series of quasi-static indentation tests on...

Load forecasting has always been a crucial component of operational and managerial aspect of efficient power system planning. Since there are several factors on which load forecasting depends, it becomes necessary to find out the level of impact these factors put on it. In the study, data preparation is performed by transforming the historical elec...

Accurate load forecasting plays a crucial role in the management and control of electrical power in distribution systems. Short-Term Load Forecasting (STLF) is particularly vital for distribution planning, as it provides precise load predictions for the immediate future. This paper introduces an innovative hybrid deep-learning model specifically de...

Electricity load forecasting is an essential part of power system planning and operation, and it is crucial to make accurate predictions. The smart grid paradigm and the new energy market necessitate better demand-side management (DSM) and more reliable end-user forecasts to system scale. This paper proposes a time-series clustering-based probabili...

To fully enhance the accuracy of electric power load forecasting, a medium-term electric power load forecasting model based on secondary decomposition (predicted model of power load based on secondary decomposition, PMPL-SD) is proposed. In the PMPL-SD algorithm, the original load data are first decomposed and reconstructed using the singular spect...

The rapid expansion of electric vehicles (EVs) has rendered the load forecasting of electric vehicle charging stations (EVCS) increasingly critical. The primary challenge in achieving precise load forecasting for EVCS lies in accounting for the nonlinear of charging behaviors, the spatial interactions among different stations, and the intricate tem...

To tackle the variability of distributed renewable energy (DRE) and the timing differences in load demand, this paper perfects the integrated layout of “source−load−storage” energy control in virtual power plants (VPPs). Introducing a comprehensive control approach for VPPs of varying ownerships, and encompassing load aggregators (LAs), a robust an...

Load forecasting is an asset for sustainable building energy management, as accurate predictions enable efficient energy consumption and contribute to decarbonisation efforts. However, data-driven models are often limited by dataset length and quality. This study investigates the effectiveness of transfer learning (TL) for load forecasting in offic...

The accuracy requirements for short-term power load forecasting have been increasing due to the rapid development of the electric power industry. Nevertheless, the short-term load exhibits both elasticity and instability characteristics, posing challenges for accurate load forecasting. Meanwhile, the traditional prediction model suffers from the is...

This study presents an efficient end-to-end (E2E) learning approach for the short-term load forecasting of hierarchically structured residential consumers based on the principles of a top-down (TD) approach. This technique employs a neural network for predicting load at lower hierarchical levels based on the aggregated one at the top. A simulation...

Behind the meter (BTM) distributed energy resources (DERs), such as photovoltaic (PV) systems, battery energy storage systems (BESSs), and electric vehicle (EV) charging infrastructures, have experienced significant growth in residential locations. Accurate load forecasting is crucial for the efficient operation and management of these resources. T...

This review paper is a foundational resource for power distribution and management decisions , thoroughly examining short-term load forecasting (STLF) models within power systems. The study categorizes these models into three groups: statistical approaches, intelligent-computing-based methods, and hybrid models. Performance indicators are compared,...

The transition to smart grids is revolutionizing the management and distribution of electrical energy. Nowadays, power systems must precisely estimate real-time loads and use adaptive regulation to operate in the era of sustainable energy. To address these issues, this paper presents a new approach—a hybrid neuro-fuzzy system—that combines neural n...

In light of recent advancements in energy storage technology, this paper introduces a sophisticated approach to planning the locations and sizes of HV/MV substations, utilizing battery energy storage systems (BESS) to optimize peak load management. Traditional substation planning, reliant on peak load forecasts, often results in substantial investm...

The Smart Grid operates autonomously, facilitating the smooth integration of diverse power generation sources into the grid, thereby ensuring a continuous, reliable, and high-quality supply of electricity to end users. One key focus within the realm of smart grid applications is the Home Energy Management System (HEMS), which holds significant impo...

Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. Among the research challenges on a global scale, the investigation of deep transfer learning (DTL) in the field of electricity load forecasting represents a fundamental effort that can inform artificial intelligence applications in general. In this pa...

This study addresses the drawbacks of traditional methods used in meter coefficient analysis, which are low accuracy and long processing time. A new method based on non-parametric analysis using the Back Propagation (BP) neural network is proposed to overcome these limitations. The study explores the classification and pattern recognition capabilit...

Integrated energy systems (IESs) can easily accommodate renewable energy resources (RESs) and improve the utilization efficiency of fossil energy by integrating various energy production, conversion, and storage technologies. However, the coupled multi-energy flows and the uncertainty of RESs bring challenges regarding optimal scheduling. Therefore...

Accurate short-term electrical load forecasting is crucial for the stable operation of power systems. Given the nonlinear, periodic, and rapidly changing characteristics of short-term power load forecasts, this paper introduces a novel forecasting method employing an Extreme Learning Machine (ELM) enhanced by an improved Dwarf Mongoose Optimization...

Virtual power plants (VPPs) have been widely recognized as a key enabler for energy system neutrality. The communication traffic of a VPP fundamentally indicates its activeness in interacting with the power system, thus providing a new dimension in depicting the behaviour characteristics of distributed energy resources in VPPs. Therefore, the predi...

With the continuous development of power system and the growth of load demand, efficient and accurate short-term load forecasting (SLTF) provides reliable guidance for power system operation and scheduling. Therefore, this paper proposes a two-stage short-term load forecasting method based on temporal convolutional network and gated recurrent unit...

This paper addresses the evolving landscape of electricity markets in Europe, with a focus on the integration of Renewable Energy Communities as introduced by the Renewable Energy Directive 2018/2001. Residents within a postal code area are highly incentivized to join a community, which enables them to exchange energy among themselves at lower proc...

With the increasing decentralization of energy supply, the need to generate and use electricity locally is growing. Energy management systems at building level can be used for this purpose. Thermal and electrical load forecasts are needed as a basis for this. The paper "Overview of the current state of research on load forecasts in the building sec...

Smart grid integration with solar energy has enormous promise for efficient and sustainable energy systems. Artificial intelligence (AI) is key in maximizing smart grids' performance, dependability, and control with solar energy integration. The seamless integration of solar energy sources is the main topic of this chapter's exploration of the many...

In the context of Integrated Energy System (IES), accurate short-term power demand forecasting is crucial for ensuring system reliability, optimizing operational efficiency through resource allocation, and supporting effective real-time decision-making in energy management. However, achieving high forecasting accuracy faces significant challenges d...

As the large-scale development of electric vehicles (EVs), accurate short-term charging load forecasting for EVs is the basis of vehicle to grid (V2G) interaction. In this paper, considering the uncertainties of EV users' charging behavior, a multi-layer Long Short-Term Memory (LSTM) model considering sliding windows and online learning is proposed...

In the present time, electricity stands as one of the most fundamental needs within human societies. This is evident in the fact that all industrial activities and a significant portion of social, economic, agricultural, and other activities rely heavily on this energy source. As a result, both the quality and continuity of electricity hold immense...

Traditionally, electricity distribution networks were designed for unidirectional power flow without the need to accommodate generation installed at the point of use. However, with the increase in Distributed Energy Resources and other Low Carbon Technologies, the role of distribution networks is changing. This shift brings challenges, including th...

The short-term power load forecasting provides an essential foundation for the dispatching management of the power system, which is crucial for enhancing economy and ensuring operational stability. To enhance the precision of the short-term power load forecasting, this paper proposes a hybrid prediction algorithm based on sparrow search algorithm (...

Energy management systems allow the Smart Grids industry to track, improve, and regulate energy use. Particularly, demand-side management is regarded as a crucial component of the entire Smart Grids system. Therefore, by aligning utility offers with customer demand, anticipating future energy demands is essential for regulating consumption. An upda...

An innovative model called InE-BiLSTM is proposed here, which combines the Informer Encoder with a bidirectional LSTM (Bi-LSTM) network. The goal is to enhance the precision and efficacy of short-term electricity load forecasting. By integrating the long-term dependency capturing capability of the informer encoder with the advantages of Bi-LSTM in...

Addressing the problems of high randomness and low prediction accuracy in short-term power load forecasting, this paper proposes a multi-featured short-term power load prediction model based on the error optimal weighting method and the improved combination prediction model. Firstly, the combined algorithm of grey correlation analysis and radial ke...

As the demand for electricity, electrification, and renewable energy rises, accurate forecasting and flexible energy management become imperative. Distribution network operators face capacity limits set by regional grids, risking economic penalties if exceeded. This study examined data-driven approaches of load forecasting to address these challeng...

This review paper represents a comprehensive review of utilizing neural networks for load forecasting, organized into four main sections. Firstly, it surveys the problem domain of load forecasting, outlining its significance in the energy sector and the main challenges. Secondly, it delivers into neural network theory and main architecture, providi...

This study presents two innovative machine learning-based models: one for daily electrical load forecasting in the State of Rio de Janeiro and another for monthly forecasting for each Light concessionaire substation in the Metropolitan Area of Rio de Janeiro (MARJ). The utilized data include (1) daily electrical load data from the National System O...

The dynamic evolution and variation of electrical loads is now a priority for optimum management and, above all, forecasting. Indeed, these dynamic load variations require computer tools able to implement optimal load forecasting models. Scientific research into automated models for forecasting electrical loads therefore represents a challenge for...

This paper presents a model predictive control for maintaining power stability in a microgrid (MG) using a machin e learning program. In this work, the historical load data of the Direct Current Microgrid (DC MG) is processed, w ith some Features Selections. Neural network (NN) training and load forecasting is used to obtain dynamic load da ta. Par...

The home energy management (HEM) sector is going through an enormous change that includes important elements like incorporating green power, enhancing efficiency through forecasting and scheduling optimization techniques, employing smart grid infrastructure, and regulating the dynamics of optimal energy trading. As a result, ecosystem players need...

The utilization of DSM (Demand-Side M a na g em e nt) has d e m o n s t r a t e d effectiveness in mitigating the consequences arising from the disruption of environmentally sustainable electricity supplies. To carry out DSM successfully, various strategies are employed, including direct burden management, load forecasting, energy stockpiling, amon...

Load forecast is the foundation of power system operation and planning. The forecast results can guide the power system economic dispatch and security analysis. In order to improve the accuracy of load forecast, this paper proposes a forecasting model based on the combination of the cuckoo search (CS) algorithm and the long short-term memory (LSTM)...

Microgrids require efficient energy management systems to optimize the operation of microgrid sources and achieve economic efficiency. Bi-level energy management model is proposed in this paper to minimize the operational cost of a grid-tied microgrid under load variations and uncertainties in renewable sources while satisfying the various technica...

With the large-scale integration of renewable energy, the traditional maintenance arrangement during the load valley period cannot satisfy the transmission demand of renewable energy generation. Simultaneously, in a market-oriented operation mode, the power dispatching control center aims to reduce the overall power purchase cost while ensuring the...

The large-scale distributed photovoltaic access leads to overvoltage, equipment overload, and difficulty in on-site consumption, which causes power to be sent back to the high voltage level, posing hidden dangers to the safe operation of the distribution network. This article proposes optimizing the medium-voltage distribution network at the cloud...