Remote work might unlock solar PV's potential of cracking the ‘Duck Curve’

Applied Energy, Год журнала: 2024, Номер 367, С. 123378 - 123378

Опубликована: Май 16, 2024

Integrating renewable energy technologies into a decentralised smart grid presents the 'Duck Curve' challenge — disparity between peak demand and solar photovoltaic (PV) yield. Smart operators still lack an effective solution to this problem, resulting in need maintain standby fossil fuel-fired plants. The COVID-19 pandemic-induced lockdowns necessitated shift remote work (work-from-home) home-based education. primary objective of study was explore mitigating strategies for duck curve by investigating notable behaviour examining effect education on PV electricity use 100 households with battery storage southwest UK. This examined 1-min granular consumption data April–August 2019 2020. findings revealed statistically significant disparities demand. Notably, there 1.4—10% decrease average from April August 2020 (during following lockdown) compared corresponding months 2019. Furthermore, household reduced 24—25%, while self-consumption systems increased 7—8% during lockdown May increase particularly prominent morning afternoon, possibly attributed growing prevalence work-from-home dynamic shifts patterns emphasised role meeting evolving needs unprecedented societal changes. Additionally, might unlock PV's potential resolving Curve', urging further investigation implications infrastructure policy development.

Язык: Английский

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Energy forecasting of the building integrated photovoltaic system based on deep learning Dragonfly-firefly algorithm

Energy, Год журнала: 2024, Номер 308, С. 132926 - 132926

Опубликована: Авг. 22, 2024

Язык: Английский

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Enhanced Coconut Yield Prediction Using Internet of Things and Deep Learning: A Bi-Directional Long Short-Term Memory Lévy Flight and Seagull Optimization Algorithm Approach

Applied Sciences, Год журнала: 2024, Номер 14(17), С. 7516 - 7516

Опубликована: Авг. 25, 2024

In coastal areas, coconuts are a common crop. Everyone from farmers to lawmakers and businesses would benefit an accurate forecast of coconut production. Internet Things (IoT) sensors strategically positioned continuously monitor the environment gather production statistics obtain agricultural output predictions. To effectively estimate prediction, this study presents enhanced deep learning classifier called Bi-directional Long Short-Term Memory (BILSTM) with integrated Lévy Flight Seagull Optimization Algorithm (LFSOA). LASSO feature selection is applied eliminate superfluous characteristics in yield estimation. further enhance estimate, optimal set hyperparameters for BILSTM tuned by LFSOA, which helps avoid overfitting issue. For results, compared against different classifiers such as Recurrent Neural Network (RNN), Random Forest Classifier (RFC), LSTM. Similarly, LFSOA-based hyperparameter tuning contrasted optimization algorithms. The outputs show that achieved accuracy, precision, recall, f1-score 98.963%, 99.026%, 99.155%, 95.758%, respectively, higher when existing methods. BILSTM-LFSOA accomplished better results statistical measures, including Root Mean Square Error (RMSE) 0.105, Squared (MSE) 0.011, Absolute (MAE) 0.094, coefficient determination (R2) 0.954, respectively. From overall analysis, proposed improves prediction achieving all performance measures models. important many stakeholders, but not limited policymakers, farmers, banks, insurance companies. As crop developing countries, forecasting will lead greater financial food security these regions.

Язык: Английский

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PredXGBR: A Machine Learning Framework for Short-Term Electrical Load Prediction

Electronics, Год журнала: 2024, Номер 13(22), С. 4521 - 4521

Опубликована: Ноя. 18, 2024

The growing demand for consumer-end electrical load is driving the need smarter management of power sector utilities. In today’s technologically advanced society, efficient energy usage critical, leaving no room waste. To prevent both electricity shortage and wastage, forecasting becomes most convenient way out. However, conventional probabilistic methods are less adaptive to acute, micro, unusual changes in trend. With recent development artificial intelligence (AI), machine learning (ML) has become popular choice due its higher accuracy based on time-, demand-, trend-based feature extractions. Thus, we propose an Extreme Gradient Boosting (XGBoost) regression-based model—PredXGBR-1, which employs short-term lag features predict hourly demand. novelty PredXGBR-1 lies focus autocorrelations enhance adaptability micro-trends fluctuations. Validation across five datasets, representing eastern western USA over a 20-year period, shows that outperforms long-term feature-based XGBoost model, PredXGBR-2, state-of-the-art recurrent neural network (RNN) long memory (LSTM) models. Specifically, achieves mean absolute percentage error (MAPE) between 0.98 1.2% R2 value 0.99, significantly surpassing PredXGBR-2’s 0.61 delivering up 86.8% improvement MAPE compared LSTM These results confirm superior performance accurately

Язык: Английский

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Identification and Correction of Abnormal, Incomplete Power Load Data in Electricity Spot Market Databases

Energies, Год журнала: 2025, Номер 18(1), С. 176 - 176

Опубликована: Янв. 3, 2025

The development of electricity spot markets necessitates more refined and accurate load forecasting capabilities to enable precise dispatch control the creation new trading products. Accurate relies on high-quality historical data, with complete data serving as cornerstone for both transactions in markets. However, at distribution network or user level often suffers from anomalies missing values. Data-driven methods have been widely adopted anomaly detection due their independence prior expert knowledge physical models. Nevertheless, single architectures struggle adapt diverse characteristics networks users, hindering effective capture patterns. This paper proposes a PLS-VAE-BiLSTM-based method identification correction by combining strengths Variational Autoencoders (VAE) Bidirectional Long Short-Term Memory Networks (BiLSTM). begins preprocessing, including normalization preliminary value imputation based Partial Least Squares (PLS). Subsequently, hybrid VAE-BiLSTM model is constructed trained loaded dataset incorporating influencing factors learn relationships between different features. Anomalies are identified corrected calculating deviation model’s reconstructed values actual Finally, validation public private datasets demonstrates that PLS-VAE-BiLSTM achieves average performance metrics 98.44% precision, 94% recall rate, 96.05% F1 score. Compared VAE-LSTM, PSO-PFCM, WTRR models, proposed exhibits superior overall performance.

Язык: Английский

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Enhanced multi-energy load forecasting via multi-task learning and GRU-attention networks in integrated energy systems

Electrical Engineering, Год журнала: 2025, Номер unknown

Опубликована: Янв. 4, 2025

Язык: Английский

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Short-Term Building Electrical Load Prediction by Peak Data Clustering and Transfer Learning Strategy

Energies, Год журнала: 2025, Номер 18(3), С. 686 - 686

Опубликована: Фев. 2, 2025

With the gradual penetration of new energy generation and storage to building side, short-term prediction power demand plays an increasingly important role in peak response supply/demand balance. The low occurring frequency electrical loads buildings leads insufficient data sampling for model training, which is currently factor affecting performance load prediction. To address this issue, by using clustering knowledge transfer from similar buildings, a forecasting method proposed. First, building’s are clustered through peak/valley analysis K-nearest neighbors categorization method, thereby addressing challenge data-sparse scenarios. Second, clusters, instance-based learning (IBTL) strategy used multi-source domains enhance target prediction’s accuracy. During process, two-stage selection applied based on Wasserstein distance locality sensitive hashing. An IBTL strategy, iTrAdaboost-Elman, designed construct predictive model. proposed validated public dataset. Results show that reduces error 49.22% (MAE) compared Elman Compared same without clustering, approach also achieves higher accuracy (1.96% vs. 2.63%, MAPE). forecast hourly/daily demands two real campus USA China, respectively. effects both analyzed detail.

Язык: Английский

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Predicting Oil Temperature in Electrical Transformers Using Neural Hierarchical Interpolation

Journal of Engineering, Год журнала: 2025, Номер 2025(1)

Опубликована: Янв. 1, 2025

Effective electricity consumption planning is critical for power distribution. Ensuring the distribution network aligns with expected demand fluctuations a challenging task influenced by various time‐related and seasonal variables. This study focuses on improving transformer oil temperature forecasting, an indicator of health, using neural hierarchical interpolation time series (NHITS) model. The NHITS model’s architecture designed to handle long‐term forecasting efficiently, making it ideal capturing extended trends in temperature. It incorporates multirate signal sampling via MaxPool layers merge predictions across different scales. proposed methodology involves two key phases: data preparation model development. In phase, (ETT) datasets are used, normalized standard scaler, essential features such as external load selected. During development trained its hyperparameters optimized optimal performance. evaluates performance under conditions, including comparison multivariate univariate series, effects short horizons, impact temporal resolution. was validated ETT dataset, our results were benchmarked against previous that employed same dataset used Informer indicate outperforms model, showing average decrease 51.37% mean squared error (MSE) 37.83% absolute (MAE). These findings highlight ability capture both short‐term characteristics data, promising solution temperatures.

Язык: Английский

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Cloud-based estimation of lithium-ion battery life for electric vehicles using equivalent circuit model and recurrent neural network

Journal of Energy Storage, Год журнала: 2025, Номер 114, С. 115718 - 115718

Опубликована: Фев. 10, 2025

Язык: Английский

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Day-Ahead Demand Response Potential Prediction in Residential Buildings with HITSKAN: A Fusion of Kolmogorov-Arnold Networks and N-HiTS

Energy and Buildings, Год журнала: 2025, Номер unknown, С. 115455 - 115455

Опубликована: Фев. 1, 2025

Язык: Английский

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