A grey-box model of next-day building thermal load prediction for energy-efficient control
TL;DR: In this paper, air temperature/relative humidity and solar radiation prediction modules are integrated with a grey-box model to get reliable prediction of the hourly building load of the next day.
Abstract: Accurate building thermal load prediction is essential to many building energy control strategies. To get reliable prediction of the hourly building load of the next day, air temperature/relative humidity and solar radiation prediction modules are integrated with a grey-box model. The regressive solar radiation module predicts the solar radiation using the forecasted cloud amount, sky condition and extreme temperatures from on-line weather stations, while the forecasted sky condition is used to correct the cloud amount forecast. The temperature/relative humidity prediction module uses a dynamic grey model (GM), which is specialized in the grey system with incomplete information. Both weather prediction modules are integrated into a building thermal load model for the on-line prediction of the building thermal load in the next day. The validation of both weather prediction modules and the on-line building thermal load prediction model are presented. Copyright © 2008 John Wiley & Sons, Ltd.
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TL;DR: In this paper, the authors present a review of recent developed models for predicting building energy consumption, which include elaborate and simplified engineering methods, statistical methods and artificial intelligence methods, and further prospects are proposed for additional research reference.
Abstract: The energy performance in buildings is influenced by many factors, such as ambient weather conditions, building structure and characteristics, the operation of sub-level components like lighting and HVAC systems, occupancy and their behavior. This complex situation makes it very difficult to accurately implement the prediction of building energy consumption. This paper reviews recently developed models for solving this problem, which include elaborate and simplified engineering methods, statistical methods and artificial intelligence methods. Previous research work concerning these models and relevant applications are introduced. Based on the analysis of previous work, further prospects are proposed for additional research reference.
1,509 citations
TL;DR: 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.
611 citations
TL;DR: In this paper, an up-to-date overview of research on application of building energy modeling methods in optimal control for single building and multiple buildings is also summarized in this paper, and different model-based and model-free optimization methods for building energy system operation are reviewed and compared.
Abstract: Buildings consume about 41.1% of primary energy and 74% of the electricity in the U.S. Better or even optimal building energy control and operation strategies provide great opportunities to reduce building energy consumption. Moreover, it is estimated by the National Energy Technology Laboratory that more than one-fourth of the 713 GW of U.S. electricity demand in 2010 could be dispatchable if only buildings could respond to that dispatch through advanced building energy control and operation strategies and smart grid infrastructure. Energy forecasting models for building energy systems are essential to building energy control and operation. Three general categories of building energy forecasting models have been reported in the literature which include white-box (physics-based), black-box (data-driven), and gray-box (combination of physics based and data-driven) modeling approaches. This paper summarizes the existing efforts in this area as well as other critical areas related to building energy modeling, such as short-term weather forecasting. An up-to-date overview of research on application of building energy modeling methods in optimal control for single building and multiple buildings is also summarized in this paper. Different model-based and model-free optimization methods for building energy system operation are reviewed and compared in this paper. Agent based modeling, as a new modeling strategy, has made a remarkable progress in distributed energy systems control and optimization in the past years. The research literature on application of agent based model in building energy system control and operation is also identified and discussed in this paper.
470 citations
Cites background or methods from "A grey-box model of next-day buildi..."
...[31] integrated their on-line gray box building energy model with on-line air temperature, relative humidity and solar radiation prediction models to increase the next day hourly building load forecasting accuracy....
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...[31] developed an on-line next day building load prediction model for building energy efficient control, by using the gray box building energy model developed in [30] and a weather forecasting model....
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TL;DR: Wang et al. as mentioned in this paper investigated the potential of one of the most promising techniques in advanced data analytics, i.e., deep learning, in predicting 24-h ahead building cooling load profiles.
462 citations
TL;DR: This paper presents a data mining (DM) based approach to developing ensemble models for predicting next-day energy consumption and peak power demand, with the aim of improving the prediction accuracy.
413 citations
References
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TL;DR: The Grey Systeni and its applications are interdisciplinary, cutting across a variety of specialized fields, and it is evident that Grey System theory stands the test of time since 1982 as mentioned in this paper.
Abstract: Grey System theory was initiated in 1982 [7]. As far as information is concerned, the systems which lack information, such as structure message, operation mechanism and behaviour document, are referred to as Grey Systems. For example, the human body, agriculture, economy, etc., are Grey Systems. Usually, on the grounds of existing grey relations, grey elements, grey numbers (denoted by 8 ) one can identify which Grey System is, where \"grey\" means poor, incomplete, uncertain, etc. The goal of Grey Systeni and its applications is to bridge the gap existing between social science and natural science. Thus, one can say that the Grey System theory is interdisciplinary, cutting across a variety of specialized fields, and it is evident that Grey System theory stands the test of time since 1982. As the case stands, the developn~ent of the Grey System-as well as theoretical topic-is coupled with clear applications of the theory in assorted fields. The conccept of the Grey System, in its theory and successful application, is now well known in China. The application fields of the Grey System involve agriculture [23, 77-81, 911, ecology [59], economy [61, 102, 103, 1041, meteorology [58, 74,911, medicine [55, 891, history [63, 641, geography [I], industry [61, earthquake [73, 87, 881, geology [76, 1 191, hydrology [98, 1 121, irrigation strategy [261, military affairs, sports [116], traffic [67], management [30, 97, 1051, material science [82, 831, environment [ 1081, biological protection [69,70], judicial system [loo], etc. Projects which have been successfully completed with the Grey System theory and its applications are as follows: 1. Regional econonlic planning for several provinces in China; 2. To forecast yields of grain for some provinces in China:
4,126 citations
TL;DR: In this article, a relationship between atmospheric transmittance and the daily range of air temperature is developed, where the relationship is Tt = A[1 −exp(exp(BΔTc)] where Tt is the daily total atmospherictransmittance, ΔT is the average air temperature, and A, B, and C are empirical coefficients, determined for a particular location from measured solar radiation data.
1,035 citations
TL;DR: In this paper, the authors present a reformulation of the Bristow-Campbell model for daily solar radiation, developed using daily observations of radiation, temperature, humidity, and precipitation, from 40 stations in contrasting climates.
726 citations
Journal Article•
TL;DR: In this article, the authors present a review of building energy simulation programs developed around the world and their evolution in analysis methods and computational power have increased the opportunity for significant improvements in the flexibility and comprehensiveness of these tools.
Abstract: Various building energy simulation programs developed around the world are reaching maturity. Many use simulation methods (and even code) that originated in the 1960s. Without substantial redesign and restructuring of the programs, continuing to expand their capabilities has become difficult, time-consuming, and prohibitively expensive. However, phenomenal advances in analysis methods and computational power have increased the opportunity for significant improvements in the flexibility and comprehensiveness of these tools.
663 citations
TL;DR: In this paper, a procedure is recommended for estimating crop water requirements that only requires the measurement of maximum and minimum temperatures, although calibrated for the Senegal River Basin using climatic data from four representative locations appears to be generally applicable for other areas without calibration.
Abstract: The Senegal River is a major natural resource in West Africa where the principal economic resources are agricultural. A proposed irrigation project will provide a significant increase in crop production and will exert a large influence on the economics of Senegal, Mauritania, and Mali. The magnitude of benefits from the project will depend upon the allocation, scheduling and managing of that portion of the water to be used for irrigating agricultural crops. A procedure is recommended for estimating crop water requirements that only requires the measurement of maximum and minimum temperatures. This procedure although calibrated for the Senegal River Basin using climatic data from four representative locations appears to be generally applicable for other areas without calibration. The importance of rainfall in supplying part of crop water requirements is described. Mean, actual dependable and effective precipitation values are compared for one location. Block farming or the planting of a single crop to mana...
420 citations