Consumption Forecasting

Consumption forecasting, primarily focused on energy (electricity, PV power, wind power, and multi-energy systems), aims to accurately predict future consumption patterns to optimize resource allocation and grid management. Current research emphasizes the use of advanced machine learning models, including deep learning architectures like Transformers, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs, particularly LSTMs and BLSTMs), and hybrid approaches combining statistical methods (e.g., ARIMA) with deep learning. These advancements improve forecasting accuracy and address challenges like handling intermittent renewable energy sources and incorporating diverse data types (e.g., weather, calendar information, textual news). The resulting improvements have significant implications for energy system planning, grid stability, and efficient resource management.

Papers

April 8, 2023

Predicting Short Term Energy Demand in Smart Grid: A Deep Learning Approach for Integrating Renewable Energy Sources in Line with SDGs 7, 9, and 13
Md Saef Ullah Miah, Junaida Sulaiman, Md. Imamul Islam, Md. Masuduzzaman, Nimay Chandra Giri, Siddhartha Bhattacharyya, Segbedji Geraldo Favi, Leo Mrsic
Deep Learning Approach Energy Consumption Power Grid Smart Grid Consumption Forecasting Sustainable Development Goal Renewable Energy Source

March 15, 2023

Forecasting Intraday Power Output by a Set of PV Systems using Recurrent Neural Networks and Physical Covariates
Pierrick Bruneau, David Fiorelli, Christian Braun, Daniel Koster
Recurrent Neural Network Relevant Covariates Consumption Forecasting Closing Set Photovoltaic System Output Distribution Neural Autoregressive Model

January 18, 2023

News and Load: A Quantitative Exploration of Natural Language Processing Applications for Forecasting Day-ahead Electricity System Demand
Yun Bai, Simon Camal, Andrea Michiorri
Important News Natural Language Processing Application Consumption Forecasting Electricity Demand Day Ahead Topic Distribution Forecasting Method Dynamic Loading

December 6, 2022

Denoising diffusion probabilistic models for probabilistic energy forecasting
Esteban Hernandez Capel, Jonathan Dumas
Generative Model Generative Adversarial Network Probabilistic Model Denoising Diffusion Probabilistic Forecast Consumption Forecasting Forecasting Competition Probabilistic Load

September 3, 2022

Tree-Based Learning in RNNs for Power Consumption Forecasting
Roberto Baviera, Pietro Manzoni
Recurrent Neural Network Traditional RNNs Consumption Forecasting Tree Learning Tree LSTMs

August 11, 2022

Comparison of Forecasting Methods of House Electricity Consumption for Honda Smart Home
Farshad Ahmadi Asl, Mehmet Bodur
Consistent Comparison Consumption Forecasting Smart Environment Smart Home Support Vector Regression Forecasting Method Electricity Consumption Home Energy Management

July 4, 2022

Deep Learning for Short-term Instant Energy Consumption Forecasting in the Manufacturing Sector
Nuno Oliveira, Norberto Sousa, Isabel Praça
Deep Learning Deep Learning Model LSTM Network Load Forecasting Manufacturing Industry Consumption Forecasting

June 14, 2022

A novel MDPSO-SVR hybrid model for feature selection in electricity consumption forecasting
Yukun Bao, Liang Shen, Xiaoyuan Zhang, Yanmei Huang, Changrui Deng
Feature Selection Energy Consumption Hybrid Model Consumption Forecasting Support Vector Regression

April 1, 2022

March 9, 2022

Transfer Learning as an Essential Tool for Digital Twins in Renewable Energy Systems
Chandana Priya Nivarthi
Machine Learning Anomaly Detection Transfer Learning Digital Twin Evolutionary Computation Consumption Forecasting Sustainable Energy

February 23, 2022

Systematic review of deep learning and machine learning for building energy
Ardabili Sina, Leila Abdolalizadeh, Csaba Mako, Bernat Torok, Mosavi Amir
Deep Learning New Machine Systematic Review Energy Policy Research Energy Management Consumption Forecasting Accurate Energy Energy Modeling Energy Load Forecasting

February 15, 2022

LIMREF: Local Interpretable Model Agnostic Rule-based Explanations for Forecasting, with an Application to Electricity Smart Meter Data
Dilini Rajapaksha, Christoph Bergmeir
Application Proficiency State of the Art Forecasting Smart Meter Local Interpretable Model Agnostic Explanation Consumption Forecasting Global Forecasting Interpretable Forecast

February 11, 2022

Electricity Consumption Forecasting for Out-of-distribution Time-of-Use Tariffs
Jyoti Narwariya, Chetan Verma, Pankaj Malhotra, Lovekesh Vig, Easwara Subramanian, Sanjay Bhat
Electricity Market Consumption Forecasting Time Varying Pricing Tariff

November 27, 2021

Federated Deep Learning in Electricity Forecasting: An MCDM Approach
Marco Repetto, Davide La Torre, Muhammad Tariq
Multi Criterion Consumption Forecasting Multi Criterion Decision Federated Deep Explanation Output Electricity Forecasting

Consumption Forecasting

Papers

Predicting Short Term Energy Demand in Smart Grid: A Deep Learning Approach for Integrating Renewable Energy Sources in Line with SDGs 7, 9, and 13

Forecasting Intraday Power Output by a Set of PV Systems using Recurrent Neural Networks and Physical Covariates

News and Load: A Quantitative Exploration of Natural Language Processing Applications for Forecasting Day-ahead Electricity System Demand

Denoising diffusion probabilistic models for probabilistic energy forecasting

Tree-Based Learning in RNNs for Power Consumption Forecasting

Comparison of Forecasting Methods of House Electricity Consumption for Honda Smart Home

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

A novel MDPSO-SVR hybrid model for feature selection in electricity consumption forecasting

Knowledge distillation with error-correcting transfer learning for wind power prediction

Synthetic Photovoltaic and Wind Power Forecasting Data

Transfer Learning as an Essential Tool for Digital Twins in Renewable Energy Systems

Systematic review of deep learning and machine learning for building energy

LIMREF: Local Interpretable Model Agnostic Rule-based Explanations for Forecasting, with an Application to Electricity Smart Meter Data

Electricity Consumption Forecasting for Out-of-distribution Time-of-Use Tariffs

Federated Deep Learning in Electricity Forecasting: An MCDM Approach