A Survey on Home Energy Management <s> 6) BUILDING ENERGY CONSUMPTION <s> Thesupport-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data.High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition. <s> BIB001 </s> A Survey on Home Energy Management <s> 6) BUILDING ENERGY CONSUMPTION <s> A number of different forecasting methods have been proposed for cooling load forecasting including historic method, real-time method, time series analysis, and artificial neural networks (ANN), but accuracy and time efficiency in prediction are a couple of contradictions to be hard to resolve for real-time traffic information prediction. In order to improve time efficiency of prediction, a new hourly cooling load prediction model and method based on Least Square Support Vector Machine (LS-SVM) is proposed in this paper. A comparison of the performance of LSSVM with back propagation neural network (BPNN) is carried out. Experiments results demonstrate that LSSVM can achieve better accuracy and generalization than the BPNN, the LSSVM predictor can reduce significantly both relative mean errors and root mean squared errors of cooling load. <s> BIB002 </s> A Survey on Home Energy Management <s> 6) BUILDING ENERGY CONSUMPTION <s> In this paper we develop a class of nonlinear generative models for high-dimensional time series. We first propose a model based on the restricted Boltzmann machine (RBM) that uses an undirected model with binary latent variables and real-valued "visible" variables. The latent and visible variables at each time step receive directed connections from the visible variables at the last few time-steps. This "conditional" RBM (CRBM) makes on-line inference efficient and allows us to use a simple approximate learning procedure. We demonstrate the power of our approach by synthesizing various sequences from a model trained on motion capture data and by performing on-line filling in of data lost during capture. We extend the CRBM in a way that preserves its most important computational properties and introduces multiplicative three-way interactions that allow the effective interaction weight between two variables to be modulated by the dynamic state of a third variable. We introduce a factoring of the implied three-way weight tensor to permit a more compact parameterization. The resulting model can capture diverse styles of motion with a single set of parameters, and the three-way interactions greatly improve its ability to blend motion styles or to transition smoothly among them. Videos and source code can be found at http://www.cs.nyu.edu/~gwtaylor/publications/jmlr2011. <s> BIB003 </s> A Survey on Home Energy Management <s> 6) BUILDING ENERGY CONSUMPTION <s> Abstract This paper introduces an easily implementable and computationally efficient, ARX ( a uto r egressive with e x ogenous, i.e., external, inputs) time and temperature indexed model for 1 h ahead building thermal load prediction. Time and temperature indexing implies that different sets of coefficients are used in the predictive equation depending on the time of the day or the ambient temperature. The indexing and proposed structure of the model follows physically motivated interpretations of the loading conditions and thermal response of the building. One of the main contributions of the proposed model is that it allows determining the dominant factors that affect the thermal load at a given time. A free and widely adopted building energy and thermal load simulation program from the U.S. Department of Energy is used to determine the prediction accuracy of the proposed model on several different benchmark-building types: a small office building, a medium office building, a midrise apartment, and a high-rise apartment. <s> BIB004 </s> A Survey on Home Energy Management <s> 6) BUILDING ENERGY CONSUMPTION <s> Some of the challenges to predict energy utilization has gained recognition in the residential sector due to the significant energy consumption in recent decades. However, the modeling of residential building energy consumption is still underdeveloped for optimal and robust solutions while this research area has become of greater relevance with significant advances in computation and simulation. Such advances include the advent of artificial intelligence research in statistical model development. Artificial neural network has emerged as a key method to address the issue of nonlinearity of building energy data and the robust calculation of large and dynamic data. The development and validation of such models on one of the TxAIRE Research houses has been demonstrated in this paper. The TxAIRE houses have been designed to serve as realistic test facilities for demonstrating new technologies. The input variables used from the house data include number of days, outdoor temperature and solar radiation while the output variables are house and heat pump energy consumption. The models based on Levenberg-Marquardt and OWO-Newton algorithms had promising results of coefficients of determination within 0.87–0.91, which is comparable to prior literature. Further work will be explored to develop a robust model for residential building application. <s> BIB005 </s> A Survey on Home Energy Management <s> 6) BUILDING ENERGY CONSUMPTION <s> Ensuring sustainability demands more efficient energy management with minimized energy wastage. Therefore, the power grid of the future should provide an unprecedented level of flexibility in energy management. To that end, intelligent decision making requires accurate predictions of future energy demand/load, both at aggregate and individual site level. Thus, energy load forecasting have received increased attention in the recent past. However, it has proven to be a difficult problem. This paper presents a novel energy load forecasting methodology based on Deep Neural Networks, specifically, Long Short Term Memory (LSTM) algorithms. The presented work investigates two LSTM based architectures: 1) standard LSTM and 2) LSTM-based Sequence to Sequence (S2S) architecture. Both methods were implemented on a benchmark data set of electricity consumption data from one residential customer. Both architectures were trained and tested on one hour and one-minute time-step resolution datasets. Experimental results showed that the standard LSTM failed at one-minute resolution data while performing well in one-hour resolution data. It was shown that S2S architecture performed well on both datasets. Further, it was shown that the presented methods produced comparable results with the other deep learning methods for energy forecasting in literature. <s> BIB006 </s> A Survey on Home Energy Management <s> 6) BUILDING ENERGY CONSUMPTION <s> In smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analysis to recent machine learning approach and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management including individual load forecasting is becoming critical. In this paper, we propose deep neural network (DNN) based load forecasting models, and apply them to demand side empirical load database. DNNs are trained by two different ways: pre-training restricted Boltzmann machine and using rectified linear unit without pre-training. DNN forecasting models are trained by individual customer's electricity consumption data and regional meteorological elements. To verify the performance of DNNs, forecasting results are compared with shallow neural network (SNN), and double seasonal Holt-Winters (DSHW) model. The mean absolute percentage error (MAPE) and relative root mean square error (RRMSE) are used for verification. The results show that DNNs exhibit accurate and robust forecasts compared to other forecasting models, e.g., MAPE and RRMSE are reduced by up to 17% and 22% compared to SNN, and 9% and 29% compared to DSHW. <s> BIB007 </s> A Survey on Home Energy Management <s> 6) BUILDING ENERGY CONSUMPTION <s> The energy consumption of residential and commercial buildings has risen steadily in recent years, an increase largely due to their HVAC systems. Expected energy loads, transportation, and storage as well as user behavior influence the quantity and quality of the energy consumed daily in buildings. However, technology is now available that can accurately monitor, collect, and store the huge amount of data involved in this process. Furthermore, this technology is capable of analyzing and exploiting such data in meaningful ways. Not surprisingly, the use of data science techniques to increase energy efficiency is currently attracting a great deal of attention and interest. This paper reviews how Data Science has been applied to address the most difficult problems faced by practitioners in the field of Energy Management, especially in the building sector. The work also discusses the challenges and opportunities that will arise with the advent of fully connected devices and new computational technologies. <s> BIB008 </s> A Survey on Home Energy Management <s> 6) BUILDING ENERGY CONSUMPTION <s> Smartgrids of the future promise unprecedented flexibility in energy management. Therefore, accurate predictions/forecasts of energy demands (loads) at individual site and aggregate level of the grid is crucial. Despite extensive research, load forecasting remains to be a difficult problem. This paper presents a load forecasting methodology based on deep learning. Specifically, the work presented in this paper investigates the effectiveness of using Convolutional Neural Networks (CNN) for performing energy load forecasting at individual building level. The presented methodology uses convolutions on historical loads. The output from the convolutional operation is fed to fully connected layers together with other pertinent information. The presented methodology was implemented on a benchmark data set of electricity consumption for a single residential customer. Results obtained from the CNN were compared against results obtained by Long Short Term Memories LSTM sequence-to-sequence (LSTM S2S), Factored Restricted Boltzmann Machines (FCRBM), “shallow” Artificial Neural Networks (ANN) and Support Vector Machines (SVM) for the same dataset. Experimental results showed that the CNN outperformed SVR while producing comparable results to the ANN and deep learning methodologies. Further testing is required to compare the performances of different deep learning architectures in load forecasting. <s> BIB009 </s> A Survey on Home Energy Management <s> 6) BUILDING ENERGY CONSUMPTION <s> Abstract Low energy buildings (LEBs) are being considered as a promising solution for the built environment to satisfy high-energy efficiency standards. The technology is based on lowering the overall heat transmission coefficient value (U-value) of the buildings envelope and increasing a heat capacity thus creating a higher thermal inertia. However, LEB introduces a large time constant compared to conventional building due to which it slows the rate of heat transfer between interior of building and outdoor environment and alters the indoor climate regardless of sudden changes in climatic conditions. Therefore, it is challenging to estimate and predict thermal energy demand for such LEBs. This work focuses on artificial intelligence (AI) model to predict energy consumption of LEB. Two kinds of AI modeling approaches: “all data” and “relevant data” are considered. The “all data” uses all available training data and “relevant data” uses a small representative day dataset and addresses the complexity of building non-linear dynamics by introducing past day climatic impacts behavior. This extraction is based on dynamic time warping pattern recognition methods. The case study consists of a French residential LEB. The numerical results showed that “relevant data” modeling approach that relies on small representative data selection has higher accuracy (R2 = 0.98; RMSE = 3.4) than “all data” modeling approach (R2 = 0.93; RMSE = 7.1) to predict heating energy load. <s> BIB010 </s> A Survey on Home Energy Management <s> 6) BUILDING ENERGY CONSUMPTION <s> Although many methods are available to forecast short-term electricity load based on small scale data sets, they may not be able to accommodate large data sets as electricity load data becomes bigger and more complex in recent years. In this paper, a novel machine learning model combining convolutional neural network with K-means clustering is proposed for short-term load forecasting with improved scalability. The large data set is clustered into subsets using K-means algorithm, then the obtained subsets are used to train the convolutional neural network. A real-world power industry data set containing more than 1.4 million of load records is used in this study and the experimental results demonstrate the effectiveness of the proposed method. <s> BIB011 </s> A Survey on Home Energy Management <s> 6) BUILDING ENERGY CONSUMPTION <s> Abstract Energy consumption models play an integral part in energy management and conservation, as it pertains to buildings. It can assist in evaluating building energy efficiency, in carrying out building commissioning, and in identifying and diagnosing building system faults. This review takes an in-depth look at energy-demand prediction models for buildings in that it delves into recent developments in building energy approaches used to predict energy usage. By enlisting current approaches to the modelling of buildings, methods for building energy simulations can be categorized into four level classes as follows: (i) data-driven approaches; (ii) physics-based approaches; (iii) large scale building energy forecasting approaches; and (iv) hybrid approaches. The focus of this review is to target the data-driven approach and large-scale building energy predicting-based approaches. Here the data driven approaches can be categorized by (1) artificial neural network based approaches; (2) clustering based approaches; (3) statistical and machine learning-based approaches; and (4) support vector machine based approaches. From there, the type of data-driven based approach is further grouped by (a) benchmarking models; (b) energy-mapping models; (c) energy forecasting models; and (d) energy profiling models. Large-scale building-energy prediction techniques is then categorized as follows: (1) white-box based approaches; (2) black-box based approaches, and (3) grey-box based approaches. The current study explores first-rate data-driven based approaches about building energy analysis for industrial, commercial, domestic, etc., within a rural and urban setting. This review paper is based on the necessity of identifying points of departure and research opportunities for urban and rural-level analyses of building level energy performance. A variety of issues are explored which include: energy performance metrics; end-use of different building types; multiple levels of granularity; and urban and rural scales. Each technique encompasses a variety of input information as well as varying calculations or simulation models along with furnishing contrasting outcomes that suggest a variety of usages. A thorough review of each technique is presented in this study. This review highlights strengths, shortcomings, and purpose of the methods of numerous data-mining based approaches. A comprehensive review of energy forecasting models that are specified in the literature part is also provided. <s> BIB012 </s> A Survey on Home Energy Management <s> 6) BUILDING ENERGY CONSUMPTION <s> tAccurate forecasting simultaneously becomes more important and more challenging due to the increasing penetration of photovoltaic (PV) systems in the built environment on the one hand, and the increasing stochastic nature of electricity consumption, e.g., through electric vehicles (EVs), on the other hand. Until recently, research has mainly focused on deterministic forecasting. However, such forecasts convey little information about the possible future state of a system and since a forecast is inherently erroneous, it is important to quantify this error. This paper therefore focuses on the recent advances in the area of probabilistic forecasting of solar power (PSPF) and load forecasting (PLF). The goal of a probabilistic forecast is to provide either a complete predictive density of the future state or to predict that the future state of a system will fall in an interval, defined by a confidence level. The aim of this paper is to analyze the state of the art and assess the different approaches in terms of their performance, but also to what extent these approaches can be generalized so that they not only perform best on the data set for which they were designed, but also on other data sets or different case studies. In addition, growing interest in net demand forecasting, i.e., demand less generation, is another important motivation to combine PSPF and PLF into one review paper and assess compatibility. One important finding is that there is no single preferred model that can be applied to any circumstance. In fact, a study has shown that the same model, with adapted parameters, applied to different case studies performed well but did not excel, when compared to models that were optimized for the specific task. Furthermore, there is need for standardization, in particular in terms of filtering night time data, normalizing results and performance metrics. <s> BIB013 </s> A Survey on Home Energy Management <s> 6) BUILDING ENERGY CONSUMPTION <s> Smart grids are the developmental trend of power systems and they have attracted much attention all over the world. Due to their complexities, and the uncertainty of the smart grid and high volume of information being collected, artificial intelligence techniques represent some of the enabling technologies for its future development and success. Owing to the decreasing cost of computing power, the profusion of data, and better algorithms, AI has entered into its new developmental stage and AI 2.0 is developing rapidly. Deep learning (DL), reinforcement learning (RL) and their combination-deep reinforcement learning (DRL) are representative methods and relatively mature methods in the family of AI 2.0. This article introduces the concept and status quo of the above three methods, summarizes their potential for application in smart grids, and provides an overview of the research work on their application in smart grids. <s> BIB014 </s> A Survey on Home Energy Management <s> 6) BUILDING ENERGY CONSUMPTION <s> This paper proposes a deep learning approach for traffic flow prediction in complex road networks. Traffic flow data from induction loop sensors are essentially a time series, which is also spatially related to traffic in different road segments. The spatio-temporal traffic data can be converted into an image where the traffic data are expressed in a 3D space with respect to space and time axes. Although convolutional neural networks (CNNs) have been showing surprising performance in understanding images, they have a major drawback. In the max pooling operation, CNNs are losing important information by locally taking the highest activation values. The inter-relationship in traffic data measured by sparsely located sensors in different time intervals should not be neglected in order to obtain accurate predictions. Thus, we propose a neural network with capsules that replaces max pooling by dynamic routing. This is the first approach that employs the capsule network on a time series forecasting problem, to our best knowledge. Moreover, an experiment on real traffic speed data measured in the Santander city of Spain demonstrates the proposed method outperforms the state-of-the-art method based on a CNN by 13.1% in terms of root mean squared error. <s> BIB015 </s> A Survey on Home Energy Management <s> 6) BUILDING ENERGY CONSUMPTION <s> The uptake of smart grid technologies and increasing deployment of smart meters have brought greater attention on the analysis of individual household electricity consumption. Within the smart grid framework, home and battery energy management systems are becoming important demand side management tools with various benefits to households, utilities and networks. Load forecasting is a vital component of these tools, as it can be used in optimizing the schedule of household appliances and energy operations. Inspired by the advancements in larger scale load forecasting, this paper proposes a novel forecast method for individual household electricity loads. Besides using smart meter data together with weather and temporal variables, which are commonly used in more conventional household load forecasting methods, this approach integrates the information contained in typical daily consumption profiles extracted by clustering and classification methods. In addition to the improvements in forecast performance, the method reveals key information about a household’s habitual load profiles and other important variables which impact household consumption. <s> BIB016 </s> A Survey on Home Energy Management <s> 6) BUILDING ENERGY CONSUMPTION <s> Probabilistic load forecasting (PLF) is of important value to grid operators, retail companies, demand response aggregators, customers, and electricity market bidders. Gaussian processes (GPs) appear to be one of the promising methods for providing probabilistic forecasts. In this paper, the log-normal process (LP) is newly introduced and compared to the conventional GP. The LP is especially designed for positive data like residential load forecasting—little regard was taken to address this issue previously. In this work, probabilisitic and deterministic error metrics were evaluated for the two methods. In addition, several kernels were compared. Each kernel encodes a different relationship between inputs. The results showed that the LP produced sharper forecasts compared with the conventional GP. Both methods produced comparable results to existing PLF methods in the literature. The LP could achieve as good mean absolute error (MAE), root mean square error (RMSE), prediction interval normalized average width (PINAW) and prediction interval coverage probability (PICP) as 2.4%, 4.5%, 13%, 82%, respectively evaluated on the normalized load data. <s> BIB017 </s> A Survey on Home Energy Management <s> 6) BUILDING ENERGY CONSUMPTION <s> Abstract This paper reviews the different aspects of modeling wind energy systems namely investment, policies, performance, and social benefits for integration in deregulated power market. The wind energy system models depend on wind resource, electrical response of wind turbine generator and the returns of economic market. The paper focuses on identifying the sub-problems and their modeling approaches. Variability of the local power system and the wind resource are chaotic and are usually difficult to model. Satisfactory and some successful algorithms are discussed in detail. Machine Learning models are presented to predict market return and described by market trend and resource forecast. This paper, proposes the representation of risk from large wind integration in unit commitment and presents regional aggregation. The review is followed by critical costs modeling for wind energy projects and market risk mitigation strategies. Finally, social impact and energy security compliance from large scale wind integration are reviewed. <s> BIB018 </s> A Survey on Home Energy Management <s> 6) BUILDING ENERGY CONSUMPTION <s> Abstract Building energy consumption modeling and forecasting is essential to address buildings energy efficiency problems and take up current challenges of human comfort, urbanization growth and the consequent energy consumption increase. In a context of integrated smart infrastructures, data-driven techniques rely on data analysis and machine learning to provide flexible methods for building energy prediction. The present paper offers a review of studies developing data-driven models for building scale applications. The prevalent methods are introduced 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. A special attention is also given to different machine learning approaches. Based on the results of this review, the latest technical improvements and research efforts are synthesized. The key role of occupants’ behavior integration in data-driven modeling is discussed. Limitations and research gaps are highlighted. Future research opportunities are also identified. <s> BIB019
Predictive models can be used to estimate electricity consumption, either for the entire building or for some specific uses, including eating, cooling, washing and dryer machines BIB012 . They can also be used to predict micro generation for sources such as solar or wind BIB013 - BIB018 . In essence, this problem can be regarded as time-series forecasting, for which machine learning-based approaches can be considered BIB008 , BIB014 , BIB013 , BIB019 : (a) Statistical and conventional regression methods, such as time-series decomposition, ARMA, AIRMA, multiple linear regression BIB004 or ordinary least squares regression  offer a balance between simplicity and performance. However, for non-linear time-series the underlying prediction error tends to be unacceptable BIB019 unless an online parameter adjusting mechanism is implemented. (b) Artificial neural networks, including multilayer perceptron and feedforward neural networks BIB005 , convolutional neural networks (CNNs) BIB014 , BIB009 , recurrent neural networks (RNNs) BIB014 , BIB006 and restricted boltzmann machines (RBMs) BIB014 , BIB003 . Concerning RNNs, long short-term memory (LSTM) architectures are effective in dealing with a variety of high complex problems BIB006 , BIB007 . The incorporation of additional layers within deep ANN architectures significantly improved generalisation performance, both for short and long terms. The main drawback of these high complex topologies is the training computational burden. (c) Capsule networks rely on the concept of a capsule, which is capable of learning implicit features over a limited domain of input deformations  ,  . They have also been considered to forecast time-series data, namely to building energy forecast BIB015 ,  . (d) Support vector machines (SVMs) BIB001 are intrinsically adapted to solve regression problems, under the so-called support vector regression (SVR) modelling. Furthermore, they provide satisfactory results even when few data samples are available BIB019 . The application of SVR-based techniques to building energy forecast has been suggested in a number of works (see e.g. BIB002 - BIB010 ), being the underlying prediction performance superior to traditional techniques and even to some ANN-based data-driven models. (e) The joint application of segmentation and regression techniques rely on the concept of shape-similar data clustering. Instead of using all available data to train a machine learning-based model, it adjusts models to capture data patterns assigned to a given cluster. In BIB011 a K-means clustering algorithm was used to segment electricity readings on a hourly basis, along with a CNN model used to approximate each cluster, while in BIB016 household electricity loads are predicted by using classification and regression trees, together with self-organising map data clustering. (f) Gaussian process regression (GPR) is a non-parametric method based on gaussian processes (GPs)  . GPs represent time-series as a collection of jointly multivariate gaussian random variables, and they are completely specified by a mean and covariance function. Given a training data set, they can be regarded as defining a set of functions that pass through the observations and are otherwise normally distributed, in log marginal likelihood sense BIB017 . (g) Ensembles of several models combine different techniques, while taking advantage of their individual strengths. They have, in the last few years, gained considerable interest in building energy consumption modelling and forecast. In some cases, they have shown to outperform regular single models, although their adoption has been hampered, in part due to implementation complexity and computational burden. Examples include random forests and boosting decision trees BIB019 .