Forecast Building Energy Consumption Research Articles

Study on deep reinforcement learning techniques for building energy consumption forecasting

Reliable and accurate building energy consumption prediction is becoming increasingly pivotal in building energy management. Currently, data-driven approach has shown promising performances and gained lots of research attention due to its efficiency and flexibility. As a combination of reinforcement learning and deep learning, deep reinforcement learning (DRL) techniques are expected to solve nonlinear and complex issues. However, very little is known about DRL techniques in forecasting building energy consumption. Therefore, this paper presents a case study of an office building using three commonly-used DRL techniques to forecast building energy consumption, namely Asynchronous Advantage Actor-Critic (A3C), Deep Deterministic Policy Gradient (DDPG) and Recurrent Deterministic Policy Gradient (RDPG). The objective is to investigate the potential of DRL techniques in building energy consumption prediction field. A comprehensive comparison between DRL models and common supervised models is also provided.The results demonstrate that the proposed DDPG and RDPG models have obvious advantages in forecasting building energy consumption compared to common supervised models, while accounting for more computation time for model training. Their prediction performances measured by mean absolute error (MAE) can be improved by 16%-24% for single-step ahead prediction, and 19%-32% for multi-step ahead prediction. The results also indicate that A3C performs poor prediction accuracy and shows much slower convergence speed than DDPG and RDPG. However, A3C is still the most efficient technique among these three DRL methods. The findings are enlightening and the proposed DRL methodologies can be positively extended to other prediction problems, e.g., wind speed prediction and electricity load prediction.

Estimates of residential building energy consumption using a multi-verse optimizer-based support vector machine with k-fold cross-validation

The ever-increasing human population, building constructions, and technology usages have currently caused electric consumption to grow significantly. Accordingly, some of the efficient tools for more and more energy saving and development are efficient energy management and forecasting energy consumption for buildings. Additionally, efficient energy management and smart restructuring can improve energy performance in different areas. Given that electricity is the main form of energy that is consumed in residential buildings, forecasting the electrical energy consumption in a building will bring significant benefits to the building and business owners. All these means call for precise energy forecast to make the best decisions. In recent years, artificial intelligence, generally, and machine learning methods, in some areas, have been employed to forecast building energy consumption and efficiency. The present study aims to predict energy consumption with higher accuracy and lower run time. We optimize the parameters of a support vector machine (SVM) using a multi-verse optimizer (MVO) without the grid search algorithm, due to the development consequence of residential energy predicting models. This paper presented the MVO-SVM approach for predicting energy consumption in residential buildings. The proposed approach examined a UCI repository dataset. Based on the experimental results MVO can effectively decrease the number of features while preserving a great predicting precision.

Support vector regression for predicting building energy consumption in southern China

It is increasingly significant to predict building energy consumption in energy-saving decision making. This paper presents the method of support vector regress (SVR) to forecast building energy consumption in southern China. To improve the reliability of SVR in building energy consumption prediction, multiple parameters including weather data such as yearly mean outdoor dry-bulb temperature, relative humidity and global solar radiation and economic factors such as the ratio of urbanization, gross domestic product, household consumption level and total area of structure are taken as inputs. The performance of SVR with respect to two parameters, c and g, is explored using the k-fold cross validation with grid searching method based on radial-basis function kernel. Finally, the performance of the proposed model is checked by comparing the prediction model with statistic data taken from Chinese National Bureau of Statistics in 4 provinces of southern China using statistical error tests such as the mean square error (MSE) and the squared correlation coefficient (r2). With the analysis based on statistical error tests, the results show that the SVR method can estimate the building energy consumption with good accuracy with MSE less than 0.001 and r2 more than 0.99.

Applying support vector machines to predict building energy consumption in China

It is increasingly significant to predict building energy consumption (BEC) in energy-saving decision making. This paper presents the method of support vector machines (SVM) to forecast building energy consumption in China. To improve the reliability of SVM in building energy consumption prediction, multiple parameters including weather data such as yearly mean outdoor dry-bulb temperature, relative humidity and global solar radiation and economic factors such as the ratio of urbanization, gross domestic product, household consumption level and total area of structure are taken as inputs. The performance of SVM with respect to two parameters, c and g, is explored using the k-fold cross validation with grid searching method based on radial-basis function kernel. Finally, the performance of the proposed model is checked by comparing the prediction model with statistic data taken from Chinese National Bureau of Statistics in 30 provinces (including autonomous regions and municipalities) of China using statistical error tests such as the mean square error (MSE) and the squared correlation coefficient (r2). With the analysis based on statistical error tests, the results show that the SVM method can estimate the building energy consumption with good accuracy with MSE less than 1E-3 and r2 more than 0.991.

Time series forecasting for building energy consumption using weighted Support Vector Regression with differential evolution optimization technique

Electricity load forecasting is crucial for effective operation and management of buildings. Support Vector Regression (SVR) have been successfully used in solving nonlinear regression and time series problems related to building energy consumption forecasting. As the performance of SVR heavily depends on the selection of its parameters, differential evolution (DE) algorithm is employed in this study to solve this problem. The forecasting model is developed using weighted SVR models with nu-SVR and epsilon-SVR. The DE algorithm is again used to determine the weights corresponding to each model. A case of time series energy consumption data from an institutional building in Singapore is used to elucidate the performance of the proposed model. The proposed model can be used to forecast both, half-hourly and daily electricity consumption time series data for the same building. The results show that for half-hourly data, the model exhibits higher weight for nu-SVR, whereas for daily data, a higher weight for epsilon-SVR is observed. The mean absolute percentage error (MAPE) for daily energy consumption data is 5.843 and that for half-hourly energy consumption is 3.767 respectively. A detailed comparison with other evolutionary algorithms show that the proposed model yields higher accuracy for building energy consumption forecasting.

Short-term building energy model recommendation system: A meta-learning approach

High-fidelity and computationally efficient energy forecasting models for building systems are needed to ensure optimal automatic operation, reduce energy consumption, and improve the building’s resilience capability to power disturbances. Various models have been developed to forecast building energy consumption. However, given buildings have different characteristics and operating conditions, model performance varies. Existing research has mainly taken a trial-and-error approach by developing multiple models and identifying the best performer for a specific building, or presumed one universal model form which is applied on different building cases. To the best of our knowledge, there does not exist a generalized system framework which can recommend appropriate models to forecast the building energy profiles based on building characteristics. To bridge this research gap, we propose a meta-learning based framework, termed Building Energy Model Recommendation System (BEMR). Based on the building’s physical features as well as statistical and time series meta-features extracted from the operational data and energy consumption data, BEMR is able to identify the most appropriate load forecasting model for each unique building. Three sets of experiments on 48 test buildings and one real building were conducted. The first experiment was to test the accuracy of BEMR when the training data and testing data cover the same condition. BEMR correctly identified the best model on 90% of the buildings. The second experiment was to test the robustness of the BEMR when the testing data is only partially covered by the training data. BEMR correctly identified the best model on 83% of the buildings. The third experiment uses a real building case to validate the proposed framework and the result shows promising applicability and extensibility. The experimental results show that BEMR is capable of adapting to a wide variety of building types ranging from a restaurant to a large office, and gives excellent performance in terms of both modeling accuracy and computational efficiency.

Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy

Buildings are the dominant source of energy consumption and environmental emissions in urban areas. Therefore, the ability to forecast and characterize building energy consumption is vital to implementing urban energy management and efficiency initiatives required to curb emissions. Advances in smart metering technology have enabled researchers to develop “sensor based” approaches to forecast building energy consumption that necessitate less input data than traditional methods. Sensor-based forecasting utilizes machine learning techniques to infer the complex relationships between consumption and influencing variables (e.g., weather, time of day, previous consumption). While sensor-based forecasting has been studied extensively for commercial buildings, there is a paucity of research applying this data-driven approach to the multi-family residential sector. In this paper, we build a sensor-based forecasting model using Support Vector Regression (SVR), a commonly used machine learning technique, and apply it to an empirical data-set from a multi-family residential building in New York City. We expand our study to examine the impact of temporal (i.e., daily, hourly, 10min intervals) and spatial (i.e., whole building, by floor, by unit) granularity have on the predictive power of our single-step model. Results indicate that sensor based forecasting models can be extended to multi-family residential buildings and that the optimal monitoring granularity occurs at the by floor level in hourly intervals. In addition to implications for the development of residential energy forecasting models, our results have practical significance for the deployment and installation of advanced smart metering devices. Ultimately, accurate and cost effective wide-scale energy prediction is a vital step towards next-generation energy efficiency initiatives, which will require not only consideration of the methods, but the scales for which data can be distilled into meaningful information.

Forecasting building energy consumption using neural networks and hybrid neuro-fuzzy system: A comparative study

AbstractAs a regular data-driven method, Artificial Neural Networks (ANNs) are popular in building energy prediction. In this paper, an alternative approach, namely, hybrid genetic algorithm-adaptive network-based fuzzy inference system (GA-ANFIS) is presented. In this model, GA optimizes the subtractive clustering’s radiuses which help form the rule base, and ANFIS adjusts the premise and consequent parameters to optimize the forecasting performance. a hierarchical structure of ANFIS is also suggested to solve the probably curse-of-dimensionality problem. The performance of the proposed model is compared with ANN using two different data sets, which are collected from the Energy Prediction Shootout I contest and a library building located in Zhejiang University, China.Results show that the hybrid GA-ANFIS model has better performance than ANN in term of prediction accuracy. The proposed model also has the same scale of modeling time as ANN if parameters in GA procedure are carefully selected. It can be regarded as an alternative method in building energy prediction.

Prediction of building energy needs in early stage of design by using ANFIS

Studies performed on the prediction of building energy consumption are increasingly important for selecting the best control strategies against the excessive energy consumptions. This paper presents Adaptive Network Based Inference System (ANFIS) model to forecast building energy consumption in a cold region. The objective of this paper is to examine the feasibility and applicability of ANFIS in building energy load forecasting area. Different combinations of building samples formed by using three different form factors (FF 1/2, FF 1/1 and FF 2/1), nine azimuth angles varied 0o–80o, three transparency ratios of 15%, 20%, 25% and five insulation thicknesses of 0, 2.5, 5, 10 and 15cm. Finally, it is observed that ANFIS can be a strong tool with the 96.5 and 83.8% for heating and cooling energy prediction in pre-design stage of energy efficient buildings for choosing the best combinations.

Forecasting building energy consumption with hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system

There are several ways to forecast building energy consumption, varying from simple regression to models based on physical principles. In this paper, a new method, namely, the hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system (GA-HANFIS) model is developed. In this model, hierarchical structure decreases the rule base dimension. Both clustering and rule base parameters are optimized by GAs and neural networks (NNs). The model is applied to predict a hotel’s daily air conditioning consumption for a period over 3 months. The results obtained by the proposed model are presented and compared with regular method of NNs, which indicates that GA-HANFIS model possesses better performance than NNs in terms of their forecasting accuracy.

Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption

There are several ways to attempt to model a building and its heat gains from external sources as well as internal ones in order to evaluate a proper operation, audit retrofit actions, and forecast energy consumption. Different techniques, varying from simple regression to models that are based on physical principles, can be used for simulation. A frequent hypothesis for all these models is that the input variables should be based on realistic data when they are available, otherwise the evaluation of energy consumption might be highly under or over estimated. In this paper, 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. The Administration Building of the University of São Paulo is used as a case study. The building energy consumption profiles are collected as well as the campus meteorological data. Results show that both models are suitable for energy consumption forecast. Additionally, a parametric analysis is carried out for the considered building on EnergyPlus in order to evaluate the influence of several parameters such as the building profile occupation and weather data on such forecasting.

Applying support vector machines to predict building energy consumption in tropical region

The methodology to predict building energy consumption is increasingly important for building energy baseline model development and measurement and verification protocol (MVP). This paper presents support vector machines (SVM), a new neural network algorithm, to forecast building energy consumption in the tropical region. The objective of this paper is to examine the feasibility and applicability of SVM in building load forecasting area. Four commercial buildings in Singapore are selected randomly as case studies. Weather data including monthly mean outdoor dry-bulb temperature ( T 0), relative humidity (RH) and global solar radiation (GSR) are taken as three input features. Mean monthly landlord utility bills are collected for developing and testing models. In addition, the performance of SVM with respect to two parameters, C and ɛ, was explored using stepwise searching method based on radial-basis function (RBF) kernel. Finally, all prediction results are found to have coefficients of variance (CV) less than 3% and percentage error (%error) within 4%.