Journal ArticleDOI
A review on time series forecasting techniques for building energy consumption
TLDR
The various combinations of the hybrid model are found to be the most effective in time series energy forecasting for building and the nine most popular forecasting techniques based on the machine learning platform are analyzed.Abstract:
Energy consumption forecasting for buildings has immense value in energy efficiency and sustainability research. Accurate energy forecasting models have numerous implications in planning and energy optimization of buildings and campuses. For new buildings, where past recorded data is unavailable, computer simulation methods are used for energy analysis and forecasting future scenarios. However, for existing buildings with historically recorded time series energy data, statistical and machine learning techniques have proved to be more accurate and quick. This study presents a comprehensive review of the existing machine learning techniques for forecasting time series energy consumption. Although the emphasis is given to a single time series data analysis, the review is not just limited to it since energy data is often co-analyzed with other time series variables like outdoor weather and indoor environmental conditions. The nine most popular forecasting techniques that are based on the machine learning platform are analyzed. An in-depth review and analysis of the ‘hybrid model’, that combines two or more forecasting techniques is also presented. The various combinations of the hybrid model are found to be the most effective in time series energy forecasting for building.read more
Citations
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Journal ArticleDOI
Status quo and opportunities for building energy prediction in limited data Context—Overview from a competition
TL;DR: In this paper , the authors proposed a representative prediction case: cross-building prediction with limited physical parameters and historical data, and a total of 195 participants formed 89 teams to participate in the competition.
Journal ArticleDOI
Time Series Forecasting of Agricultural Products’ Sales Volumes Based on Seasonal Long Short-Term Memory
Tae-Woong Yoo,Il-Seok Oh +1 more
TL;DR: The experimental results show that the error rate of the proposed seasonal long short-term memory model is significantly lower than those of other classical methods.
Journal ArticleDOI
Energy Consumption Forecasting for the Digital-Twin Model of the Building
TL;DR: The Prophet model using information about the total energy consumption and real data about the energy consumption of the top 10 energy-consuming devices gave the best forecast of energy consumption for the following day.
Proceedings ArticleDOI
A Review of Single Artificial Neural Network Models for Electricity Spot Price Forecasting
Fan Zhang,Hasan Fleyeh +1 more
TL;DR: In this paper, a review of recent applications of neural networks for electricity price forecasting is presented and a summary of the strengths and weaknesses of each approach is presented, followed by reviews of the corresponding studies of each neural network with respect to electricity forecasting from year 2010 onwards.
Journal ArticleDOI
Household Energy Consumption Prediction Using the Stationary Wavelet Transform and Transformers
TL;DR: In this article , a self-attention mechanism was used to learn complex patterns and dynamics from household power consumption data to forecast power consumption in different resolutions, and a hybrid approach was proposed to achieve superior prediction performance compared to existing power consumption prediction methods.
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