数据驱动的洞察以支持知情决策：应用LSTM网络进行利比亚鲁班市电网稳定和能源规划的鲁棒电力预测

An Asymmetric Loss with Anomaly Detection LSTM Framework for Power Consumption Prediction

Building an accurate load forecasting model with minimal underpredictions is vital to prevent any undesired power outages due to underproduction of electricity. However, the power consumption patterns of the residential sector contain fluctuations and anomalies making them challenging to predict. In this paper, we propose multiple Long Short-Term Memory (LSTM) frameworks with different asymmetric loss functions to impose a higher penalty on underpredictions. We also apply a density-based spatial clustering of applications with noise (DBSCAN) anomaly detection approach, prior to the load foreca…

使用LSTM网络进行阿根廷短期区域电力需求预测

Short-Term Regional Electricity Demand Forecasting in Argentina Using LSTM Networks

本研究提出基于LSTM网络的深度学习模型，用于预测阿根廷科尔多瓦的短期每小时电力需求，通过整合外部变量实现高精度预测，并进行了可解释性及极值时间预测分析，为电网运营提供支持。

A Hybrid Model for Forecasting Short-Term Electricity Demand

Currently the UK Electric market is guided by load (demand) forecasts published every thirty minutes by the regulator. A key factor in predicting demand is weather conditions, with forecasts published every hour. We present HYENA: a hybrid predictive model that combines feature engineering (selection of the candidate predictor features), mobile-window predictors and finally LSTM encoder-decoders to achieve higher accuracy with respect to mainstream models from the literature. HYENA decreased MAPE loss by 16\% and RMSE loss by 10\% over the best available benchmark model, thus establishing a ne…

Wholesale Electricity Price Forecasting using Integrated Long-term Recurrent Convolutional Network Model

Electricity price is a key factor affecting the decision-making for all market participants. Accurate forecasting of electricity prices is very important and is also very challenging since electricity price is highly volatile due to various factors. This paper proposes an integrated long-term recurrent convolutional network (ILRCN) model to predict electricity prices considering the majority contributing attributes to the market price as input. The proposed ILRCN model combines the functionalities of convolutional neural network and long short-term memory (LSTM) algorithm along with the propos…

基于混合统计与机器学习算法的电力需求预测：乌克兰案例研究

Electricity Demand Forecasting with Hybrid Statistical and Machine Learning Algorithms: Case Study of Ukraine

本文针对乌克兰电力需求预测问题，提出一种融合宏观经济回归、ARIMA、ARMA与LSTM的混合模型，在17520个时间步的样本外预测中达到96.83%的准确率。

使用CNN与堆叠BiLSTM的达卡市短期电力需求预测

Short-Term Electricity Demand Forecasting of Dhaka City Using CNN with Stacked BiLSTM

本文提出CNN与堆叠BiLSTM混合模型用于达卡市短期电力需求预测，并研究归一化技术，在MAPE、MSE、RMSE和MAE指标上优于LSTM、CNN-BiLSTM和CNN-LSTM等基准模型。

存在电动汽车充电场景下的单户家庭区间负荷预测

Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging

针对现有家庭负荷预测多排除带家用充电桩住户、仅提供无不确定性信息的点预测的问题，提出结合LSTM与贝叶斯推理dropout层的LSTM-BNNs方法，实现带预测区间的高精度预测，且验证了疫情封控对模型性能无显著影响。

基于时间序列基础模型的外生数据电力需求预测评估

Assessing Electricity Demand Forecasting with Exogenous Data in Time Series Foundation Models

本文评估了不同基础模型在电力需求预测中的表现，发现基础模型在某些情况下不如简单基线，强调了模型架构和地理环境对预测效果的影响。

ERCOT SCENT区域中期电力负荷预测中由可解释性驱动的特征工程

Explainability-Driven Feature Engineering for Mid-Term Electricity Load Forecasting in ERCOT's SCENT Region

针对电力系统中期负荷预测需求，对比多种机器学习模型，并引入SHAP方法量化特征贡献以指导特征工程，同时提升模型可解释性与预测准确率。

Recurrent Neural Based Electricity Load Forecasting of G-20 Members

Forecasting the actual amount of electricity with respect to the need/demand of the load is always been a challenging task for each power plants based generating stations. Due to uncertain demand of electricity at receiving end of station causes several challenges such as: reduction in performance parameters of generating and receiving end stations, minimization in revenue, increases the jeopardize for the utility to predict the future energy need for a company etc. With this issues, the precise forecasting of load at the receiving end station is very consequential parameter to establish the i…

DA-LSTM：一种用于区间负荷预测的动态漂移自适应学习框架

DA-LSTM: A Dynamic Drift-Adaptive Learning Framework for Interval Load Forecasting with LSTM Networks

针对负荷预测中概念漂移幅度动态变化的问题，提出DA-LSTM框架，无需预设漂移阈值，通过主动与被动自适应策略提升预测性能，并在真实云环境中验证了其有效性。

使用Temporal Fusion Transformer进行短期电力负荷预测：电网层级和数据源的影响

Short-Term Electricity Load Forecasting Using the Temporal Fusion Transformer: Effect of Grid Hierarchies and Data Sources

本文研究了Temporal Fusion Transformer在不同时间范围和电网层级进行短期电力负荷预测的潜力，发现其在变电站层级日前预测和周前预测中显著优于LSTM模型。

基于长短期记忆（LSTM）深度学习模型的土耳其水力发电量预测

Hydroelectric Generation Forecasting with Long Short Term Memory (LSTM) Based Deep Learning Model for Turkey

本研究针对土耳其水力发电量预测问题，构建基于LSTM的深度学习模型，通过不同时间跨度数据与模型层数的对比实验，确定最优参数配置，为长期水电发电规划提供支撑。

面向大学建筑日前电力预测的机器学习与时间序列基础模型

Machine Learning and Time-Series Foundation Models for Day-Ahead Electricity Forecasting in a University Building

该研究针对大学建筑日前电力预测，对比统计模型、机器学习模型、RNN及TimesFM等模型，发现TimesFM 3.0搭载协变量时误差最低，XGBoost具竞争力，TimesFM无需建筑专属权重训练，协变量对节假日预测有帮助。

A Hybrid Residual Dilated LSTM end Exponential Smoothing Model for Mid-Term Electric Load Forecasting

This work presents a hybrid and hierarchical deep learning model for mid-term load forecasting. The model combines exponential smoothing (ETS), advanced Long Short-Term Memory (LSTM) and ensembling. ETS extracts dynamically the main components of each individual time series and enables the model to learn their representation. Multi-layer LSTM is equipped with dilated recurrent skip connections and a spatial shortcut path from lower layers to allow the model to better capture long-term seasonal relationships and ensure more efficient training. A common learning procedure for LSTM and ETS, with …

短期电力负荷预测误差的元回归分析

Meta-Regression Analysis of Errors in Short-Term Electricity Load Forecasting

本文通过元回归分析59项研究中421个短期电力负荷预测模型，发现电网层级、预测粒度和算法显著影响MAPE，而数据集规模等无显著影响，并指出LSTM及神经网络组合方法表现最佳。

Smart Metering System Capable of Anomaly Detection by Bi-directional LSTM Autoencoder

Anomaly detection is concerned with a wide range of applications such as fault detection, system monitoring, and event detection. Identifying anomalies from metering data obtained from smart metering system is a critical task to enhance reliability, stability, and efficiency of the power system. This paper presents an anomaly detection process to find outliers observed in the smart metering system. In the proposed approach, bi-directional long short-term memory (BiLSTM) based autoencoder is used and finds the anomalous data point. It calculates the reconstruction error through autoencoder with…

基于预测的混合与堆叠有状态/无状态模型综合分析

Comprehensive Forecasting-Based Analysis of Hybrid and Stacked Stateful/ Stateless Models

本文针对密西西比州立大学机场站点短期风速预测，系统比较堆叠有状态/无状态LSTM与GRU四种模型的架构、RMSE表现及时间空间复杂度。

混合 Kolmogorov-Arnold 网络与 XGBoost 框架用于澳大利亚国家电力市场的周 ahead 电价预测

Hybrid Kolmogorov-Arnold Network and XGBoost Framework for Week-Ahead Price Forecasting in Australia's National Electricity Market

本文提出了一种混合 KAN+XGBoost 框架，用于预测澳大利亚国家电力市场的周 ahead 电价，该框架结合了 Kolmogorov-Arnold 网络的全局非线性表示能力和 XGBoost 的局部鲁棒性，以捕捉长期依赖和短期价格波动，实验表明该模型在 MAE 上比 XGBoost 和 naive 基线模型分别减少了 12% 和 50% 以上。

Deep Learning for Short-term Instant Energy Consumption Forecasting in the Manufacturing Sector

Electricity is a volatile power source that requires great planning and resource management for both short and long term. More specifically, in the short-term, accurate instant energy consumption forecasting contributes greatly to improve the efficiency of buildings, opening new avenues for the adoption of renewable energy. In that regard, data-driven approaches, namely the ones based on machine learning, are begin to be preferred over more traditional ones since they provide not only more simplified ways of deployment but also state of the art results. In that sense, this work applies and com…

Short-term Prediction of Household Electricity Consumption Using Customized LSTM and GRU Models

With the evolution of power systems as it is becoming more intelligent and interactive system while increasing in flexibility with a larger penetration of renewable energy sources, demand prediction on a short-term resolution will inevitably become more and more crucial in designing and managing the future grid, especially when it comes to an individual household level. Projecting the demand for electricity for a single energy user, as opposed to the aggregated power consumption of residential load on a wide scale, is difficult because of a considerable number of volatile and uncertain factors…

Predicting Electricity Consumption using Deep Recurrent Neural Networks

Electricity consumption has increased exponentially during the past few decades. This increase is heavily burdening the electricity distributors. Therefore, predicting the future demand for electricity consumption will provide an upper hand to the electricity distributor. Predicting electricity consumption requires many parameters. The paper presents two approaches with one using a Recurrent Neural Network (RNN) and another one using a Long Short Term Memory (LSTM) network, which only considers the previous electricity consumption to predict the future electricity consumption. These models wer…

智能电网时代的负荷预测：机遇与先进机器学习模型

Load Forecasting in the Era of Smart Grids: Opportunities and Advanced Machine Learning Models

本研究针对智能电网中的短期负荷预测，评估了XGBoost、LightGBM、LSTM、GRU及混合模型，并采用皮尔逊相关系数分析外生变量，实验证明机器学习模型优于ARIMA基线。

基于LSTM网络的自适应在线学习用于能源价格预测

Adaptive Online Learning with LSTM Networks for Energy Price Prediction

本研究提出结合自定义损失函数的LSTM网络自适应在线学习框架，用于加州日前电价预测，其性能优于静态模型，为电力市场决策提供工具。

Day-Ahead PV Power Forecasting Based on MSTL-TFT

In recent years, renewable energy resources have accounted for an increasing share of electricity energy.Among them, photovoltaic (PV) power generation has received broad attention due to its economic and environmental benefits.Accurate PV generation forecasts can reduce power dispatch from the grid, thus increasing the supplier's profit in the day-ahead electricity market.The power system of a PV site is affected by solar radiation, PV plant properties and meteorological factors, resulting in uncertainty in its power output.This study used multiple seasonal-trend decomposition using LOESS (MS…

未来电网状态下的电力需求预测：一项基于数字孪生的仿真研究

Electricity Demand Forecasting in Future Grid States: A Digital Twin-Based Simulation Study

本研究利用德国3511户家庭34个月智能电表数据和数字孪生仿真，证明LSTM在日前电力需求预测中比传统综合负荷曲线最多降低68.5%的均方根误差，并指出未来电网状态下所有方法性能下降，需推动机器学习方法的应用与改进。

时空网格智能：一种融合图神经网络和LSTM的混合框架用于鲁棒的电力盗窃检测

Spatio-Temporal Grid Intelligence: A Hybrid Graph Neural Network and LSTM Framework for Robust Electricity Theft Detection

本文提出一种融合时间序列异常检测、监督学习和图神经网络的混合框架，用于在不平衡数据集上高精度检测电力盗窃，实验表明该方法在准确率、召回率和精确率上均优于单一模型。

Short-term Load Forecasting with Distributed Long Short-Term Memory

With the employment of smart meters, massive data on consumer behaviour can be collected by retailers. From the collected data, the retailers may obtain the household profile information and implement demand response. While retailers prefer to acquire a model as accurate as possible among different customers, there are two major challenges. First, different retailers in the retail market do not share their consumer's electricity consumption data as these data are regarded as their assets, which has led to the problem of data island. Second, the electricity load data are highly heterogeneous si…

数据驱动的日前市场电价预测：聚焦短训练集窗口

Data-driven Day Ahead Market Prices Forecasting: A Focus on Short Training Set Windows

本研究针对短训练窗口下的电力日前市场电价预测问题，对比四种机器学习模型在三个欧洲市场的表现，发现LightGBM在45/60天窗口下精度与鲁棒性最优，可有效支持数据稀缺波动环境的电价预测。

Energy Forecasting in Smart Grid Systems: A Review of the State-of-the-art Techniques

Energy forecasting has a vital role to play in smart grid (SG) systems involving various applications such as demand-side management, load shedding, and optimum dispatch. Managing efficient forecasting while ensuring the least possible prediction error is one of the main challenges posed in the grid today, considering the uncertainty and granularity in SG data. This paper presents a comprehensive and application-oriented review of state-of-the-art forecasting methods for SG systems along with recent developments in probabilistic deep learning (PDL) considering different models and architecture…

Spatial-temporal wind field prediction by Artificial Neural Networks

The prediction of near surface wind speed is becoming increasingly vital for the operation of electrical energy grids as the capacity of installed wind power grows. The majority of predictive wind speed modeling has focused on point-based time-series forecasting. Effectively balancing demand and supply in the presence of distributed wind turbine electricity generation, however, requires the prediction of wind fields in space and time. Additionally, predictions of full wind fields are particularly useful for future power planning such as the optimization of electricity power supply systems. In …

用于澳大利亚全国电力市场的短期电价预测的机器学习和深度学习模型

Machine Learning and Deep Learning Models for Short Term Electricity Price Forecasting in Australia's National Electricity Market

本文比较了六种算法在澳大利亚电力市场价格预测中的表现，发现树模型在预测中表现更优，但所有模型的误差均较高，需进一步改进。

多变量对抗性时间序列预测模型

Multi-variable Adversarial Time-Series Forecast Model

针对工业电力系统预测忽略保护性电表数据的问题，提出多变量对抗性时间序列预测模型，通过对抗过程正则化LSTM以同时预测多类型变量并平衡单变量精度与变量间关系，在真实工业数据上取得更优预测精度。

面向产消者社区的电力负荷人工智能预测

AI-Powered Predictions for Electricity Load in Prosumer Communities

本文针对产消者社区短期需求响应中的负荷预测问题，测试了Prophet、LSTM、SARIMA、Holt-Winters及经验回归等多种人工智能预测方法，发现结合持久项与回归项的方法预测精度最佳。

Industrial Forecasting with Exponentially Smoothed Recurrent Neural Networks

Time series modeling has entered an era of unprecedented growth in the size and complexity of data which require new modeling approaches. While many new general purpose machine learning approaches have emerged, they remain poorly understand and irreconcilable with more traditional statistical modeling approaches. We present a general class of exponential smoothed recurrent neural networks (RNNs) which are well suited to modeling non-stationary dynamical systems arising in industrial applications. In particular, we analyze their capacity to characterize the non-linear partial autocorrelation st…

Reinforcement Learning-Based Cooperative P2P Power Trading between DC Nanogrid Clusters with Wind and PV Energy Resources

In replacing fossil fuels with renewable energy resources for carbon neutrality, the unbalanced resource production of intermittent wind and photovoltaic (PV) power is a critical issue for peer-to-peer (P2P) power trading. To address this issue, a reinforcement learning (RL) technique is introduced in this paper. For RL, a graph convolutional network (GCN) and a bi-directional long short-term memory (Bi-LSTM) network are jointly applied to P2P power trading between nanogrid clusters, based on cooperative game theory. The flexible and reliable DC nanogrid is suitable for integrating renewable e…

DeepKoopFormer：一种基于Koopman增强Transformer的时间序列预测架构

DeepKoopFormer: A Koopman Enhanced Transformer Based Architecture for Time Series Forecasting

DeepKoopFormer结合Transformer与Koopman算子理论，通过谱约束线性动态和结构正则化实现稳定可解释的高维时间序列预测，在多个数据集上优于LSTM和基线Transformer。

Pattern-based Long Short-term Memory for Mid-term Electrical Load Forecasting

This work presents a Long Short-Term Memory (LSTM) network for forecasting a monthly electricity demand time series with a one-year horizon. The novelty of this work is the use of pattern representation of the seasonal time series as an alternative to decomposition. Pattern representation simplifies the complex nonlinear and nonstationary time series, filtering out the trend and equalizing variance. Two types of patterns are defined: x-pattern and y-pattern. The former requires additional forecasting for the coding variables. The latter determines the coding variables from the process history.…

面向校园能源韧性的机器学习：智能减载中的聚类与时间序列预测

Machine Learning for Campus Energy Resilience: Clustering and Time-Series Forecasting in Intelligent Load Shedding

本研究针对拉各斯大学提出基于机器学习的智能减载框架，通过主成分分析降维和Mini-Batch K-Means聚类划分建筑需求等级，并结合Prophet等模型进行短期负荷预测，实现更公平、数据驱动的配电优化。

短期电力负荷预测的多频重建扩散模型

Short-term electricity load forecasting with multi-frequency reconstruction diffusion

本文提出多频重建扩散模型，通过多频数据重构和去噪网络提升短期电力负荷预测精度。

基于混合Transformer-XGBoost框架的新英格兰短期电力需求预测：融合天气、日历和COVID-19指标

Short-Term Electricity Demand Forecasting for New England: A Comprehensive Machine Learning Benchmark with Weather, Calendar, and COVID-19 Indicators

提出混合Transformer-XGBoost框架，集成天气、日历和COVID-19变量，用于新英格兰短期电力需求预测，测试RMSE为8876 MWh，MAPE为2.05%，并发现COVID-19特征在疫情后成为噪声。

Q-DPTS：基于变分量子线路的量子差分隐私时间序列预测

Q-DPTS: Quantum Differentially Private Time Series Forecasting via Variational Quantum Circuits

本文提出混合量子-经典框架 Q-DPTS，将变分量子线路与差分隐私机制结合用于时间序列预测，在 ETT 数据集上相比经典和量子基线在相同隐私预算下取得更低预测误差，展示了量子增强差分隐私预测的潜力。

基于GAT-LSTM的增强型负荷预测：利用电网与时间特征

Enhanced Load Forecasting with GAT-LSTM: Leveraging Grid and Temporal Features

针对可再生能源增加电网负荷预测复杂性的问题，本文提出融合GAT与LSTM的GAT-LSTM混合模型，通过纳入边属性和早期融合时空特征提升预测性能，在巴西电力系统数据集上较先进模型显著降低误差，为电网管理提供有力工具。

电力消耗预测：一种使用SHapley加法解释的协作集成学习方法

Electricity Consumption Forecasting: An Approach Using Cooperative Ensemble Learning with SHapley Additive exPlanations

提出一种名为弱分离器增强器（WSB）的协作集成学习方法，结合LSTM、RF、SVR和XGBoost模型，利用SHAP进行特征选择，遗传算法和粒子群优化超参数，对巴西联邦学院两个校区未来12个月的电力消耗进行预测，取得较低误差。

适用于野火易发地区的广义多因素深度学习电力负荷预测模型

A generalised multi-factor deep learning electricity load forecasting model for wildfire-prone areas

本文提出基于GRU的多因素深度学习模型，利用输入特征选择和领先温度关系预测野火季节配电网负荷，在维多利亚州8个网络上显著优于LSTM并带来节能潜力。

面向电力批发市场鲁棒短期公用事业需求预测的深度强化学习辅助联邦学习

Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets

针对电力批发市场中发电厂无法直接获取公用事业公司数据、联邦学习训练STLF模型易因缺陷性能下降的问题，提出DRL辅助的DearFSAC方法，结合LSTM、自编码器实现鲁棒准确的短期负荷预测，经真实数据验证性能优于其他方法。

使用粒子群优化的多头注意力增强CNN-LSTM网络进行短期负荷预测

Short-Term Load Forecasting Using A Particle-Swarm Optimized Multi-Head Attention-Augmented CNN-LSTM Network

本文提出粒子群优化的多头注意力增强CNN-LSTM网络，用于短期负荷预测，通过自动超参数优化和显著特征识别，在真实电力需求数据上取得1.9376的MAPE，显著优于现有方法。

Appliance Level Short-term Load Forecasting via Recurrent Neural Network

Accurate load forecasting is critical for electricity market operations and other real-time decision-making tasks in power systems. This paper considers the short-term load forecasting (STLF) problem for residential customers within a community. Existing STLF work mainly focuses on forecasting the aggregated load for either a feeder system or a single customer, but few efforts have been made on forecasting the load at individual appliance level. In this work, we present an STLF algorithm for efficiently predicting the power consumption of individual electrical appliances. The proposed method b…

海洋可再生能源功率的综合预测：一种用于风-光-波浪能的自适应贝叶斯优化 MVMD-LSTM 框架

Integrated Forecasting of Marine Renewable Power: An Adaptively Bayesian-Optimized MVMD-LSTM Framework for Wind-Solar-Wave Energy

针对风-光-波浪海洋能源多源耦合与非线性预测难题，提出贝叶斯优化的 MVMD-LSTM 一体化框架，实现自动参数搜索并显著提升超短期功率预测精度。

智能能源卫士：一种用于检测光伏发电欺诈的混合深度学习模型

Smart Energy Guardian: A Hybrid Deep Learning Model for Detecting Fraudulent PV Generation

针对智慧城市住宅光伏发电中的窃电问题，提出一种结合多尺度CNN、LSTM和Transformer的混合深度学习模型，并引入数据嵌入技术，有效提升复杂窃电检测的准确性。

基于Tube损失的深度网络用于提升风速概率预测

Tube Loss based Deep Networks For Improving the Probabilistic Forecasting of Wind Speed

本文利用Tube损失函数构建深度概率预测模型（整合LSTM、GRU、TCN），并设计δ参数调优启发式，在三个风速数据集上实现了更窄且更可靠的预测区间，提升了风速概率预测性能。

一种基于伽辽金近似库普曼伪谱的电网负荷曲线预测可解释方法

An Interpretable Approach to Load Profile Forecasting in Power Grids using Galerkin-Approximated Koopman Pseudospectra

本文提出一种基于算子理论的可解释机器学习方法，通过库普曼算子分解电网负荷动态为相干时空模式，在大规模数据集上比LSTM更准确高效且具可解释性。

智能电表数据的再假名化策略对深度学习画像攻击不具备鲁棒性

Re-pseudonymization Strategies for Smart Meter Data Are Not Robust to Deep Learning Profiling Attacks

本文提出首个针对再假名化智能电表数据的深度学习画像攻击，结合神经网络嵌入与最近邻分类，在 5139 个家庭中识别准确率达 73.4%，证明频繁再假名化策略仍可被逆转。

深度学习模型在日前负荷预测中的比较评估：探究关键精度驱动因素

A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers

本文对葡萄牙全国日前负荷预测开展比较研究，评估MLP、LSTM、N-BEATS、TCN和TFT五种深度自回归模型，发现N-BEATS精度最优、MLP次之，并识别出小时和温度等关键精度驱动因素。