Journal ArticleDOI
A review on time series forecasting techniques for building energy consumption
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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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Predicting residential energy consumption using CNN-LSTM neural networks
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Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges
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Journal ArticleDOI
Modeling and forecasting building energy consumption: A review of data-driven techniques
TL;DR: A review of studies developing data-driven models for building scale applications with a focus on the input data characteristics and data pre-processing methods, the building typologies considered, the targeted energy end-uses and forecasting horizons, and accuracy assessment.
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Urban heat island impacts on building energy consumption: A review of approaches and findings
TL;DR: In this paper, the authors reviewed existing literature for improving the understanding of UHI impacts on building energy consumption and found that UHI could result in a median increase of 19.0% in cooling energy consumption, and a median decrease of 18.7% in heating energy consumption.
References
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Journal ArticleDOI
Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption
TL;DR: In this article, a comparison is made between a simple model based on artificial neural network (ANN) and a model that is based on physical principles (EnergyPlus) as an auditing and predicting tool in order to forecast building energy consumption.
Journal ArticleDOI
A comparison of univariate methods for forecasting electricity demand up to a day ahead
TL;DR: In this article, the authors compared the performance of six univariate methods for short-term electricity demand forecasting for lead times up to a day ahead and concluded that simpler and more robust methods can outperform more complex alternatives.
Journal ArticleDOI
Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy
TL;DR: In this paper, a sensor-based forecasting model using Support Vector Regression (SVR), a commonly used machine learning technique, was applied to an empirical data-set from a multi-family residential building in Manhattan.
Journal ArticleDOI
How effective are neural networks at forecasting and prediction? A review and evaluation
Monica Adya,Fred Collopy +1 more
TL;DR: Evaluating research in this area has been difficult, due to lack of clear criteria, so eleven guidelines that could be used in evaluating this literature are identified and used.
Journal ArticleDOI
Short-Term Load Forecasting Using General Exponential Smoothing
Abstract: An essential component of a comprehensive, real-time control center for power systems is a method for the calculation of short-term load forecasts. This paper explores the use of general exponential smoothing to develop an adaptive forecasting system based on observed values of integrated hourly demand. A model is developed which offers high accuracy and operational simplicity. Forecasts of hourly MWH load with lead times of one to twenty-four hours are computed at hourly intervals throughout the week. A pragmatic viewpoint is maintained throughout the paper, which includes an analysis of two years of hourly load data, test results for the method developed, and discussions of adjustments for holidays and weather disturbances.
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