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In the past few years, there has been a notable interest in the application of machine learning methods to enhance energy efficiency in the smart building industry. The paper discusses the use of machine learning in smart buildings to improve energy efficiency by analyzing data on energy usage, occupancy patterns, and environmental conditions. The study focuses on implementing and evaluating energy consumption prediction models using algorithms like long short-term memory (LSTM), random forest, and gradient boosting regressor. Real-life case studies on educational buildings are conducted to assess the practical applicability of these models. The data is rigorously analyzed and preprocessed, and performance metrics such as root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to compare the effectiveness of the algorithms. The results highlight the importance of tailoring predictive models to the specific characteristics of each building's energy consumption. [ABSTRACT FROM AUTHOR]

Building energy baseline models, particularly machine learning-based models, are a core aspect in the evaluation of building energy performance to identify inefficient energy consumption behavior. In smart city design, energy planners and decision makers require comprehensive information on energy consumption across diverse building types as well as comparisons between different types of buildings. However, there is no comprehensive study of baseline modeling across the main building types to help identify factors that influence the performance of different machine learning algorithms for baseline modeling. Therefore, the goal of this paper is to review and analyze energy consumption behavior and evaluate the prediction performance and interpretability of machine learning-based baseline modeling techniques across major building types. The results have shown that the Extreme Gradient Boosting Machine (XGBoost) model is the most accurate baseline modeling method for all building types. Time-related factors, especially the week of the year and the day of the week, have the most impact on energy consumption across all building types. This study is presented as a useful resource for smart city energy managers to help in choosing and setting up appropriate methodologies for better operational effectiveness and efficiencies when designing and planning smart energy systems. [ABSTRACT FROM AUTHOR]

Nowadays, supermarkets and retail stores all use software systems with databases to store customer transactions. Over time, the volume of data is also increasing and it contributes a lot of hidden value in this data warehouse, mining data from historical transactions will find out the buying patterns and behavior of consumers, which can assist in improving sales by reaching customers more precisely. Data-mining techniques allow us to exploit synthetic information in many aspects, such as association rules for statistics and decision support in many fields. Most users of e-commerce systems or web platforms are concerned about privacy protection, such as privacy requirements for name, occupation, age, interests, residence, or sales transactions on the e-commerce system. Therefore, protecting the privacy of electronic service users in data mining is also an important factor that needs to be considered. For those important reasons, the Apriori algorithm was researched and extrapolated into a new S-Apriori algorithm for the concept of seasonal shopping. This paper applied the S-Apriori, ORM model, SQL language, and C# to build the libraries for the forecast of Seasonal Consumption Behavior of Consumers. Also, a new Thanh and Huh Cryptography algorithm for privacy-preserving filters is proposed for data-mining processing privacy protection. The paper experimented on two datasets based on a small dataset with 37 records and the Adventure large dataset of Microsoft with 172,459 records, while the software provides association rules with the corresponding confidence ratio for users to easily make decisions. In addition, the model will be packaged and published to the Microsoft Nuget ecosystem, developers and researchers can use it to develop association rule mining systems or further extend it based on the new S-Apriori model. [ABSTRACT FROM AUTHOR]

Accurately and efficiently predicting the fuel consumption of vehicles is the key to improving their fuel economy. This paper provides a comprehensive review of data-driven fuel consumption prediction models. Firstly, by classifying and summarizing relevant data that affect fuel consumption, it was pointed out that commonly used data currently involve three aspects: vehicle performance, driving behavior, and driving environment. Then, from the model structure, the predictive energy and the characteristics of the traditional machine learning model (support vector machine, random forest), the neural network model (artificial neural network and deep neural network), and this paper point out that: (1) the prediction model of fuel consumption based on neural networks has a higher data processing ability, higher training speed, and stable prediction ability; (2) by combining the advantages of different models to build a hybrid model for fuel consumption prediction, the prediction accuracy of fuel consumption can be greatly improved; (3) when comparing the relevant indicts, both the neural network method and the hybrid model consistently exhibit a coefficient of determination above 0.90 and a root mean square error below 0.40. Finally, the summary and prospect analysis are given based on various models' predictive performance and application status. [ABSTRACT FROM AUTHOR]

Cloud Computing is a paradigm allowing access to physical and application resources online via the Internet. These resources are virtualized using virtualization software to make them available to users as a service. Virtual machines (VMs) migration technique provided by virtualization technology impacts the performance of the cloud. It is a significant concern in this environment. When allocating resources, the distribution of VMs is unbalanced, and their movement from one server to another can increase energy consumption and network overhead, necessitating an improvement in VM migrations. This paper addresses the VMs migration issue by applying a machine learning model to reduce the VMs migration number and energy consumption. The proposed algorithm (named VMLM) is based on improving VM's migration process and selection. It has been benchmarked with JVCMMD and EVSP solutions. The simulation results demonstrate the efficiency of our proposal, which includes two phases the machine learning preparing stage and the VMs migration stage. [ABSTRACT FROM AUTHOR]

With the continuous growth of the film market and the consumption demand of audiences, the value of film content has become increasingly apparent. Extracting movie content elements is an important step in quantitative analysis of movie content. In this paper, based on text mining technology, deep data analysis of movie reviews is carried out using TF-IDF and machine learning to visualize high-frequency words, and sentiment analysis is performed on reviews to find out the hidden deep information behind the big data of movie reviews. Although the extracted keywords can draw the content and characteristics of the film for us, they cannot establish the correlation with the creative elements of the film and television works. Therefore, in this paper, the extracted keywords are clustered to find the central words of these words. First of all, in order to improve the representativeness of keywords, the Epoch data set and the Batchsize data set are used to conduct experimental analysis on the model in this paper. Through comparative experiments, it is concluded that when Batchsize = 32 and Epoch = 25, the model achieves the optimal classification effect. The analysis shows that when the training times are too small, the model is not fully learned, and when the training times are too many, the model will be overfitted, which will reduce the accuracy of the model, resulting in the opposite effect. The keywords extracted above can draw the content and characteristics of the movie for us. [ABSTRACT FROM AUTHOR]

In the last decade, with the introduction of smart meters to smart grids, demand response programs (DRPs) have been widely adopted to establish a generation and consumption balance. DRPs provide many benefits for efficient grid management. However, these programs are conducive to higher levels of dissatisfaction by changing grid customers' consumption patterns. This paper aims to investigate the effects of DRPs on social welfare (SW). To this end, the paper presents a mathematical model for SW during the implementation of DRPs. In the proposed model, the level of customer satisfaction is assumed the main factor contributing to SW. This mathematical model considers different types of DRPs in terms of their impacts on SW. The paper also seeks to obtain linear and nonlinear models of DRPs and the coefficient of participation (CoP). CoP as an indicator shows the percentage of customers who actively participate in each DRP and plays a significant role in the assessment of the SW level. Moreover, owing to the sparsity and variety of distribution network customers, load patterns are classified into different clusters to take the load types into account. As a matter of fact, this process aims to identify similar patterns and thus, the same level of satisfaction for each separate cluster. The classification process is performed by using a machine learning-based clustering method known as the Affinity Propagation (AP) algorithm. Then, the model calculates the level of SW for the clusters based on the usage of electrical equipment and the time of day when they are turned on. The obtained levels of SW help operators select the best programs for every cluster in terms of customer satisfaction, and achieve the highest performance of DRPs. Lastly, the model is evaluated using real data of a distribution network to ensure the effectiveness and accuracy of the model. [Display omitted] • Development of a mathematical model for assessing Social Welfare during DRPs execution, considering customer satisfaction. • Integration of Coefficient of Participation (CoP) as an important factor in evaluating SW during DRP implementation. • Development of Affinity Propagation algorithm to cluster load patterns, enabling tailored DRPs to maximize effectiveness. [ABSTRACT FROM AUTHOR]

As embedded devices start supporting heterogeneous processing cores (Central Processing Unit [CPU]–Graphical Processing Unit [GPU] based cores), performance aware task allocation becomes a major issue. Use of Open Computing Language (OpenCL) applications on both CPU and GPU cores improves performance and resolves the problem. However, it has an adverse effect on the overall power consumption and the operating temperature of the system. Operating both kind of cores within a small form factor at high frequency causes rise in power consumption which in turn leads to increase in processor temperature. The elevated temperature brings about major thermal issues. In this paper, we present our investigation on the role of CPU during execution of GPU specific application and argue against running it at the high frequency. In addition, a machine learning guided mechanism to predict the optimal operating frequency of CPU cores during execution of OpenCL GPU kernels is presented in this study. Our experiments with OpenCL applications on the state of the art ODROID XU4 embedded platform show that the CPU cores of the experimental board if operated at a frequency proposed by our Machine Learning‐based predictive method brings about 12.5°C reduction in processor temperature at 1.06% degradation in performance compared to the baseline frequency (default performance frequency governor of the embedded platform). [ABSTRACT FROM AUTHOR]

Load shapes derived from smart meter data are frequently employed to analyze daily energy consumption patterns, particularly in the context of applications like Demand Response (DR). Nevertheless, one of the most important challenges to this endeavor lies in identifying the most suitable consumer clusters with similar consumption behaviors. In this paper, we present a novel machine learning based framework in order to achieve optimal load profiling through a real case study, utilizing data from almost 5000 households in London. Four widely used clustering algorithms are applied specifically K-means, K-medoids, Hierarchical Agglomerative Clustering and Density-based Spatial Clustering. An empirical analysis as well as multiple evaluation metrics are leveraged to assess those algorithms. Following that, we redefine the problem as a probabilistic classification one, with the classifier emulating the behavior of a clustering algorithm, leveraging Explainable AI (xAI) to enhance the interpretability of our solution. According to the clustering algorithm analysis the optimal number of clusters for this case is seven. Despite that, our methodology shows that two of the clusters, almost 10% of the dataset, exhibit significant internal dissimilarity. As a result, these clusters have been excluded from consideration for DR programs. The scalability and versatility of our solution makes it an ideal choice for power utility companies aiming to segment their users for creating more targeted DR programs. • Assessment of four ML clustering algorithms of different nature. • Re-definition of the problem using probabilistic classification and xAI. • Optimization of demand-side management clusters combining ML and empirical knowledge. • Experimental application on historical data from 4438 households in London. [ABSTRACT FROM AUTHOR]

In predicting energy consumption, classic econometric and statistical models are used to forecast energy consumption. These models may have limitations in an increasingly fast-changing energy market, which requires big data analysis of energy consumption patterns and relevant variables using complex mathematical tools. In current literature, there are minimal comparison studies reviewing machine learning algorithms to predict energy consumption in Korea. To bridge this gap, this paper compared three different machine learning algorithms, namely the Random Forest (RF) model, XGBoost (XGB) model, and Long Short-Term Memory (LSTM) model. These algorithms were applied in Period 1 (prior to the onset of the COVID-19 pandemic) and Period 2 (after the onset of the COVID-19 pandemic). Period 1 was characterized by an upward trend in energy consumption, while Period 2 showed a reduction in energy consumption. LSTM performed best in its prediction power specifically in Period 1, and RF outperformed the other models in Period 2. Findings, therefore, suggested the applicability of machine learning to forecast energy consumption and also demonstrated that traditional econometric approaches may outperform machine learning when there is less unknown irregularity in the time series, but machine learning can work better with unexpected irregular time series data. [ABSTRACT FROM AUTHOR]

• Variable-length motif discovery is used to detect power waveform motifs (PWMs) for the first time in unsupervised NILM. • An adaptive similarity threshold setting method is proposed for establishing an unsupervised motif discovery method. • Frequent sequence pattern mining is employed to learn the complete power patterns in appliance working cycles. • The proposed method is integrated into the incremental learning framework, which can gradually discover "unknown" appliances. • Comparison results on public and private datasets demonstrate our work performs better on complex appliance identification. Due to the diversity of appliances and users' power consumption behaviors, it is challenging to accurately extract load signature samples for non-intrusive load monitoring (NILM) in unseen scenarios. To this end, this paper proposes an enhanced NILM load pattern extraction method via variable-length motif discovery. Firstly, the variable-length motif discovery method is used for the first time to discover similar subsequences from the aggregated power time series to extract power waveform signature (PWS) samples for appliances and obtain the power waveform motifs (PWMs). Furthermore, the appliance PWM sequence pattern mining method is proposed to detect complete power consumption patterns of appliance working cycles, and eventually construct the load signature library. Finally, the similarity between the original PWS samples and the templates in the signature library is measured to realize load identification. In practice, the proposed method is intended to integrate into the incremental learning framework, so that the unknown appliances can be identified gradually by continuous iterative learning. The comparison testing results on the public and private datasets demonstrate that the proposed method can effectively discover various PWMs of different appliances, and accurately model their power consumption patterns, thus exhibiting better performance on the unsupervised load identification compared with the existing methods, especially for the complex appliances. [ABSTRACT FROM AUTHOR]

• Unbalanced residential energy consumption data classification problem is tackled. • Urban vs rural energy consumption is classified in original and reduced feature space. • Monte Carlo under-sampling is used to obtain distribution of classification performance metrics. • Six family of classifiers were compared using 6 classification metrics for unbalanced data. • Hyperparameter tuning and optimal classifier pipeline were obtained using the genetic programming optimizer. Energy consumer locations are required for framing effective energy policies. However, due to privacy concerns, it is becoming increasingly difficult to obtain the locational data of the consumers. Machine learning (ML) based classification strategies can be used to find the locational information of the consumers based on their historical energy consumption patterns. The ML methods in this paper are applied to the Residential Energy Consumption Survey 2009 dataset. In this dataset, the number of consumers in the urban area is higher than the rural area, thus making the classification problem unbalanced. The unbalanced classification problem has been solved in original and transformed or reduced feature space using Monte Carlo based under-sampling of the majority class datapoints. The hyperparameters for each classification algorithm family is represented as an optimized pipeline, obtained using the genetic programming (GP) optimizer. The classification performance metrics are then obtained for different algorithm families on the original and transformed feature spaces. Performance comparisons have been reported using univariate and bivariate distributions of the classification metrics viz. accuracy, geometric mean score (GMS), F 1 score, precision, area under the curve (AUC) of receiver operator characteristics (ROC). The energy policy aspects for the urban and rural residential consumers based on the classification results have also been discussed. [ABSTRACT FROM AUTHOR]

The evolution towards Smart Grids (SGs) represents an important opportunity for modernization of the energy industry. It is characterized by a bidirectional flow of information and energy between consumers and suppliers. However, the rapid increase of energy demands in residential areas is becoming a challenging problem. In order to address this issue, Demand-Side Management (DSM) has proven to be an effective solution. In this paper, we propose LOSISH, a price-based Demand Response (DR) system for load scheduling in residential Smart Homes (SHs) that achieves a trade-off between electricity payments and consumer's discomfort. Our proposed system considers Renewable Energy Sources (RESs), Battery Energy Storage System (BESS) and Plug-in Electric Vehicle (PEV). We formulate our scheduling as a constrained optimization problem and we propose a new hybrid algorithm to solve it. The latter combines two well known heuristic algorithms: Particle Swarm Optimization (PSO) and Binary Particle Swarm Optimization (BPSO). Moreover, we propose a new clustering algorithm based on Machine Learning (ML) to extract consumer's preferences from a real dataset that contains the historical consumption patterns of his smart appliances. We test our approach on real data traces obtained from a SH and we set up an experiment to evaluate our algorithm on a Raspberry Pi and measure its energy consumption. To prove the effectiveness of our approach, we compare our results with another approach from the literature in terms of electricity bill, Peak-to-Average Ratio (PAR), energy consumption, and execution time. Numerical results show that LOSISH outperforms the other approach in terms of electricity bill (up to 52.92% cheaper), PAR (up to 44% decrease in peak demands), energy consumption (up to 69.44% less consumption), and execution time (up to 63.15% faster). • The load scheduling problem in residential smart homes is studied. • A constrained problem is formulated to make a trade-off between costs and comfort. • A hybrid heuristic algorithm and a Machine Learning clustering model are proposed. • Real data collected from a smart home in the UK was used to validate our approach. • Simulation and experimental results demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

In recent years, a large number of building energy consumption prediction models, with various intended uses, have been proposed. The majority of these models have either taken a data-driven or a physical modeling approach, with each approach having its own strengths and limitations. Towards leveraging the strengths and reducing the limitations of each approach for improved prediction performance, this paper presents a hybrid machine-learning approach for occupant-behavior-sensitive energy consumption prediction. The proposed approach is composed of three constituent models: (1) a machine-learning model that learns the impact of outdoor weather conditions from simulation-generated data, (2) a machine-learning model that learns the impact of occupant behavior from real data, and (3) an ensemble model that predicts cooling energy consumption based on the outputs of the first two models. The simulation-generated data were created through simulating a set of reference buildings in EnergyPlus. The real data were collected from an office building in Pennsylvania. The proposed hybrid model was validated on an unseen real dataset. It achieved 0.73 kWh RMSE and 9.07% CV in hourly cooling energy consumption prediction, which indicates that the proposed approach is promising. [ABSTRACT FROM AUTHOR]

Heavy-duty trucks contribute approximately 20% of fuel consumption in the United States of America (USA). The fuel economy of heavy-duty vehicles (HDV) is affected by several real-world parameters like road parameters, driver behavior, weather conditions, and vehicle parameters, etc. Although modern vehicles comply with emissions regulations, potential malfunction of the engine, regular wear and tear, or other factors could affect vehicle performance. Predicting fuel consumption per trip based on dynamic on-road data can help the automotive industry to reduce the cost and time for on-road testing. Data modeling can easily help to diagnose the reason behind fuel consumption with a knowledge of input parameters. In this paper, an artificial neural network (ANN) was implemented to model fuel consumption in modern heavy-duty trucks for predicting the total and instantaneous fuel consumption of a trip based on very few key parameters, such as engine load (%), engine speed (rpm), and vehicle speed (km/h). Instantaneous fuel consumption data can help to predict patterns in fuel consumption for optimized fleet operations. In this work, the data used for modeling was collected at a frequency of 1Hz during on-road testing of modern heavy-duty vehicles (HDV) at the West Virginia University Center for Alternative Fuels Engines and Emissions (WVU CAFEE) using the portable emissions monitoring system (PEMS). The performance of the artificial neural network was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The model was further evaluated with data collected from a vehicle on-road trip. The study shows that artificial neural networks performed slightly better than other machine learning techniques such as linear regression (LR), and random forest (RF), with high R-squared (R2) and lower root mean square error. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, more and more people, especially young people, begin to use credit card with the changing of consumption concept in China so that the business on credit cards is growing fast. Therefore, it is significative that some effective tools such as credit-scoring models are created to help those decision makers engaged in credit cards. A novel credit-scoring model, called vertical bagging decision trees model (abbreviated to VBDTM), is proposed for the purpose in this paper. The model is a new bagging method that is different from the traditional bagging. The VBDTM model gets an aggregation of classifiers by means of the combination of predictive attributes. In the VBDTM model, all train samples and just parts of attributes take part in learning of every classifier. By contrast, classifiers are trained with the sample subsets in the traditional bagging method and every classifier has the same attributes. The VBDTM has been tested by two credit databases from the UCI Machine Learning Repository, and the analysis results show that the performance of the method proposed by us is outstanding on the prediction accuracy. [ABSTRACT FROM AUTHOR]

In Energy Performance Contracts (EPC), savings resulting from building and energy system retrofit are estimated as the difference between the actual consumption an prediction from a baseline model. This paper proposes an automated method to select the most relevant baseline model. In addition to commonly used cooling degree days (CDD), climatic variables mixing temperature and humidity are introduced, such as dew point or moist air enthalpy. In terms of prediction algorithm, 11 algorithms, linear and nonlinear, are compared. The novel methodology enables an energy analyst to easily find the most relevant model for a specific building, with a focus on both precision and seasonal bias. By applying the novel methodology on 11 buildings under real EPCs, the result shows a decrease of prediction error by 23.5% with respect to models referenced in the contracts. Lastly, the proposed method is generalized to address the case where contracts involve multiple buildings of different types or consumption ranges, with the ability to identify a common best model by using new dimensionless indicators, and reach an 11% improvement in prediction error. [ABSTRACT FROM AUTHOR]

• The system aims to minimize the gas consumption of domestic boilers for heating. • Neural Networks and a Genetic Algorithm are used for the optimal boiler operation. • Target is to find the optimal control instructions to be sent to the boiler. • The model leads mostly to lower boiler modulation, namely lower water temperatures. • Significant gas consumption reduction attained with the operation of the system. This paper presents a novel approach that aims to minimize the natural gas consumption of domestic boilers for heating purposes, while not compromising the user's heating needs. The system utilizes gas consumption data, the conditions both inside and outside the house collected via interconnected commercial thermostats, and the heating needs of the users. The architecture of the presented system is divided in two main coordinated processes: (a) the first one consists of two Neural Networks with Convolutional and Long Short-Term Memory layers, used to predict the indoor temperature and the boiler's modulation (load percentage), whereas (b) the second process includes a Genetic Algorithm used to determine the optimal operation conditions of the boiler, by finding the boiler control instructions that meet the user's heating preferences concerning the target indoor temperature, while minimizing the total gas consumption. One main advantage of the solution is its ability to consider boilers as a black box, since it does not need to be aware of the internal mechanics. In this way, the proposed methodology can be applied to a wide range of domestic gas boilers with minimum adjustments. The overall methodology is applied to four domestic boilers in Greece spanning three cities, to capture different climatic conditions and evaluate the system performance in varying outdoor conditions. The attained results indicate that the proposed system can lead to significant gas consumption reduction through autonomously created optimal control instructions provided to the boiler. [ABSTRACT FROM AUTHOR]

[Display omitted] • The communal areas can have different levels of energy performance in the 15 types of public residential buildings under study. • Spatial design features can evidently affect the energy consumption in the communal areas. • The ability to harvest daylight serves as a key determinant of energy efficiency. • Both econometric and machine learning techniques are proved to be effective to generate reliable findings. In this paper, we construct a unique database for 1228 residential buildings in Hong Kong to investigate how the spatial features of these residential buildings affect the electricity consumption in the communal area. We choose Hong Kong for this analysis as the city owns a large number of standard-type residential buildings managed by the public institution, which could be affected strongly by environmental policies. Both the machine learning method, based on the Least Absolute Shrinkage and Selection Operator (LASSO), and econometric regressions are adopted to analyse the data. We first utilize the machine learning LASSO technique to identify the most relevant factors for the subsequent econometric analysis. Our results show that the electricity demand for relatively low consumption building types, such as Twin Tower, is 6% lower than that of the high consumption building types. Newly constructed buildings usually belong to the medium consumption types, with the estimated monthly electricity consumption per apartment in communal areas to be around 50.2 kWh on average in 2020. These findings shed light on the nexus between spatial features and energy use for complex buildings, potentially contributing to the better crafting of energy-saving policy and the improvement of residential building programmes. [ABSTRACT FROM AUTHOR]

• The combination of rough set theory and deep learning algorithms is analyzed. • Rough set theory is used to reduce the influencing factors of building energy consumption. • Deep learning is used to extract features of building energy consumption data. • An accuracy comparison of several prediction models based on rough sets and different neural networks is made. The efficient and accurate prediction of building energy consumption can improve the management of power systems. In this paper, the rough set theory was used to reduce the redundant influencing factors of building energy consumption and find the critical factors of building energy consumption. These key factors were then used as the input of a deep neural network with a "deep" architecture and powerful capabilities in extracting features. Building energy consumption is output of the deep neural network. This study collected data from 100 civil public buildings for rough set reduction, and then collected data from a laboratory building of a university in Dalian for nearly a year to train and test deep neural networks. The test included both the short-term and medium-term predictions of building energy consumption. The prediction results of the deep neural network were compared with that of the back propagation neural network, Elman neural network and fuzzy neural network. The results show that the integrated rough set and deep neural network was the most accurate. The method proposed in this study could provide a practical and accurate solution for building energy consumption prediction. [ABSTRACT FROM AUTHOR]

Due to the availability of smart metering infrastructure, high-resolution electric consumption data is readily available to study the dynamics of residential electric consumption at finely resolved spatial and temporal scales. Analyzing the electric consumption data enables the policymakers and building owners to understand consumer's demand-consumption behaviors. Furthermore, analysis and accurate forecasting of electric consumption are substantial for consumer involvement in time-of-use tariffs, critical peak pricing, and consumer-specific demand response initiatives. Alongside its vast economic and sustainability implications, such as energy wastage and decarbonization of the energy sector, accurate consumption forecasting facilitates power system planning and stable grid operations. Energy consumption forecasting is an active research area; despite the abundance of devised models, electric consumption forecasting in residential buildings remains challenging due to high occupant energy use behavior variability. Hence the search for an appropriate model for accurate electric consumption forecasting is ever continuing. To this aim, this paper presents a spatial and temporal ensemble forecasting model for short-term electric consumption forecasting. The proposed work involves exploring electric consumption profiles at the apartment level through cluster analysis based on the k-means algorithm. The ensemble forecasting model consists of two deep learning models; Long Short-Term Memory Unit (LSTM) and Gated Recurrent Unit (GRU). First, the apartment-level historical electric consumption data is clustered. Later the clusters are aggregated based on consumption profiles of consumers. At the building and floor level, the ensemble models are trained using aggregated electric consumption data. The proposed ensemble model forecasts the electric consumption at three spatial scales apartment, building, and floor level for hourly, daily, and weekly forecasting horizon. Furthermore, the impact of spatial-temporal granularity and cluster analysis on the prediction accuracy is analyzed. The dataset used in this study comprises high-resolution electric consumption data acquired through smart meters recorded on an hourly basis over the period of one year. The consumption data belongs to four multifamily residential buildings situated in an urban area of South Korea. To prove the effectiveness of our proposed forecasting model, we compared our model with widely known machine learning models and deep learning variants. The results achieved by our proposed ensemble scheme verify that model has learned the sequential behavior of electric consumption by producing superior performance with the lowest MAPE of 4.182 and 4.54 at building and floor level prediction, respectively. The experimental findings suggest that the model has efficiently captured the dynamic electric consumption characteristics to exploit ensemble model diversities and achieved lower forecasting error. The proposed ensemble forecasting scheme is well suited for predictive modeling and short-term load forecasting. [ABSTRACT FROM AUTHOR]