Residential load forecasting using wavelet and collaborative representation transforms
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Summary
A new forecasting framework is proposed in this work which uses the extra appliance measurements in meter-level for short-term electrical load forecasting and the load features extracted from the lagged load variable vector provide superior forecasting performance especially with extra appliances load data.
- Type
- article
- Published
- 2019-11-01
- Cited by
- 64
- References
- 36
- OpenAlex
- https://openalex.org/W2956702988
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:199089098
Keywords
Representation (politics), Wavelet, Computer science, Electrical load, Variable (mathematics)
References
- Household Electricity Demand Forecast Based on Context Information and User Daily Schedule Analysis From Meter Data
- Collaborative Representation for Hyperspectral Anomaly Detection
- A sparse heteroscedastic model for the probabilistic load forecasting in energy-intensive enterprises
- Using Smart Meter Data to Improve the Accuracy of Intraday Load Forecasting Considering Customer Behavior Similarities
- High impedance fault detection based on wavelet transform and statistical pattern recognition
- Short-term load forecasting based on least square support vector machine combined with fuzzy control
- Forecasting electricity load with advanced wavelet neural networks
- Probabilistic electric load forecasting: A tutorial review
- Group-based chaos genetic algorithm and non-linear ensemble of neural networks for short-term load forecasting
- An Efficient Approach to Short-Term Load Forecasting at the Distribution Level
- Electricity, water, and natural gas consumption of a residential house in Canada from 2012 to 2014
- Long-term load forecast modelling using a fuzzy logic approach
- A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression With Hybrid Parameters Optimization
- An Accurate and Fast Converging Short-Term Load Forecasting Model for Industrial Applications in a Smart Grid
- Electrical Load Forecasting Using An Expanded Kalman Filter Bank Methodology
- A GPU deep learning metaheuristic based model for time series forecasting
- Empirical mode decomposition based denoising method with support vector regression for time series prediction: A case study for electricity load forecasting
- Deep Learning for Household Load Forecasting—A Novel Pooling Deep RNN
- Short-Term Residential Load Forecasting Based on Resident Behaviour Learning
- An efficient deep model for day-ahead electricity load forecasting with stacked denoising auto-encoders
Cited by
- Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption
- A Hybrid LSTM Neural Network for Energy Consumption Forecasting of Individual Households
- Holt–Winters smoothing enhanced by fruit fly optimization algorithm to forecast monthly electricity consumption
- Hybrid event-, mechanism- and data-driven prediction of blast furnace gas generation
- Electricity Demand Prediction Using Fractal Dimension of Load Sequence
- Air-Conditioning Load Forecasting for Prosumer Based on Meta Ensemble Learning
- Machine learning driven smart electric power systems: Current trends and new perspectives
- Short-term Load Forecasting based on Wavelet Approach
- The Method for Extraction and Identification of Non-intrusive Household Appliances Load Features Based on WPT Algorithm
- A novel hybrid model based on combined preprocessing method and advanced optimization algorithm for power load forecasting
- Correlation based Convolutional Recurrent Network for Load Forecasting
- Deep neural network for load forecasting centred on architecture evolution
- Review of Deep Learning Application for Short-Term Household Load Forecasting
- A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings
- Convolutional and recurrent neural network based model for short-term load forecasting
- A Novel Load Forecasting Approach Based on Smart Meter Data Using Advance Preprocessing and Hybrid Deep Learning
- Electrical load-temperature CNN for residential load forecasting
- A bottom-up short-term residential load forecasting approach based on appliance characteristic analysis and multi-task learning
- Residential load forecasting based on LSTM fusing self-attention mechanism with pooling
- A meta-analytic approach for determining the success factors for energy conservation
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