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Journal ArticleDOI

An Ensemble Energy Consumption Forecasting Model Based on Spatial-Temporal Clustering Analysis in Residential Buildings

23 May 2021-Energies (Multidisciplinary Digital Publishing Institute)-Vol. 14, Iss: 11, pp 3020
TL;DR: A spatial and temporal ensemble forecasting model for short-term electric consumption forecasting that has efficiently captured the dynamic electric consumption characteristics to exploit ensemble model diversities and achieved lower forecasting error is presented.
Abstract: 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.
Citations
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Journal ArticleDOI
TL;DR: The performance comparison results show that the proposed ensemble model-based intrusion detection significantly improves the intrusion detection accuracy.
Abstract: The connectivity of our surrounding objects to the internet plays a tremendous role in our daily lives. Many network applications have been developed in every domain of life, including business, healthcare, smart homes, and smart cities, to name a few. As these network applications provide a wide range of services for large user groups, the network intruders are prone to developing intrusion skills for attack and malicious compliance. Therefore, safeguarding network applications and things connected to the internet has always been a point of interest for researchers. Many studies propose solutions for intrusion detection systems and intrusion prevention systems. Network communities have produced benchmark datasets available for researchers to improve the accuracy of intrusion detection systems. The scientific community has presented data mining and machine learning-based mechanisms to detect intrusion with high classification accuracy. This paper presents an intrusion detection system based on the ensemble of prediction and learning mechanisms to improve anomaly detection accuracy in a network intrusion environment. The learning mechanism is based on automated machine learning, and the prediction model is based on the Kalman filter. Performance analysis of the proposed intrusion detection system is evaluated using publicly available intrusion datasets UNSW-NB15 and CICIDS2017. The proposed model-based intrusion detection accuracy for the UNSW-NB15 dataset is 98.801 percent, and the CICIDS2017 dataset is 97.02 percent. The performance comparison results show that the proposed ensemble model-based intrusion detection significantly improves the intrusion detection accuracy.

29 citations

Journal ArticleDOI
TL;DR: The results indicated that research on blockchain applications is still relatively new and fragmented with regard to several topics, and guidelines for further research on Blockchain applications in the AEC industry were provided.
Abstract: Blockchain is regarded as a potential technology for transforming the architecture, engineering, and construction (AEC) industry, and the number of related publications is increasing rapidly. However, a systematic review of blockchain applications in the AEC industry is lacking. The objective of this study was to review the current status of blockchain applications via a bibliometric analysis combined with a systematic literature review. According to related articles collected from databases, the present status of blockchain was analysed with regard to the distribution of articles over publication years, journals, institutions, countries, cooperation networks between authors, keyword co-occurrence networks, and research methodologies. The results indicated that research on blockchain applications is still relatively new and fragmented with regard to several topics. Five areas of benefit were identified: (i) supply chain management, (ii) contract management, (iii) information management, (iv) stakeholder management, and (v) integration management. On the basis of the technology–organisation–environment framework, nine types of challenges were identified. Future research opportunities were proposed according to the research findings. This study contributes to the current body of knowledge and provides guidelines for further research on blockchain applications in the AEC industry.

15 citations

Journal ArticleDOI
22 Nov 2021-Energies
TL;DR: In this article, an optimal number of households would be selected adaptively, and the total aggregated residential load of the selected households is used for load prediction, and ordering points to identify the clustering structure (OPTICS) algorithm is also selected to cluster households with similar power consumption patterns adaptively.
Abstract: Short-term residential load forecasting is the precondition of the day-ahead and intra-day scheduling strategy of the household microgrid. Existing short-term electric load forecasting methods are mainly used to obtain regional power load for system-level power dispatch. Due to the high volatility, strong randomness, and weak regularity of the residential load of a single household, the mean absolute percentage error (MAPE) of the traditional methods forecasting results would be too big to be used for home energy management. With the increase in the total number of households, the aggregated load becomes more and more stable, and the cyclical pattern of the aggregated load becomes more and more distinct. In the meantime, the maximum daily load does not increase linearly with the increase in households in a small area. Therefore, in our proposed short-term residential load forecasting method, an optimal number of households would be selected adaptively, and the total aggregated residential load of the selected households is used for load prediction. In addition, ordering points to identify the clustering structure (OPTICS) algorithm are also selected to cluster households with similar power consumption patterns adaptively. It can be used to enhance the periodic regularity of the aggregated load in alternative. The aggregated residential load and encoded external factors are then used to predict the load in the next half an hour. The long short-term memory (LSTM) deep learning algorithm is used in the prediction because of its inherited ability to maintain historical data regularity in the forecasting process. The experimental data have verified the effectiveness and accuracy of our proposed method.

14 citations

Journal ArticleDOI
25 Nov 2022-Energies
TL;DR: In this article , the authors provide an overview of the methods used to predict electricity supply requirements to different objects, taking into account the forecast classification according to the anticipation period, in this way, methods used in operative, short-term, medium-term and long-term forecasting have been considered.
Abstract: Balancing the production and consumption of electricity is an urgent task. Its implementation largely depends on the means and methods of planning electricity production. Forecasting is one of the planning tools since the availability of an accurate forecast is a mechanism for increasing the validity of management decisions. This study provides an overview of the methods used to predict electricity supply requirements to different objects. The methods have been reviewed analytically, taking into account the forecast classification according to the anticipation period. In this way, the methods used in operative, short-term, medium-term, and long-term forecasting have been considered. Both classical and modern forecasting methods have been identified when forecasting electric energy consumption. Classical forecasting methods are based on the theory of regression and statistical analysis (regression, autoregressive models); probabilistic forecasting methods and modern forecasting methods use classical and deep-machine-learning algorithms, rank analysis methodology, fuzzy set theory, singular spectral analysis, wavelet transformations, Gray models, etc. Due to the need to take into account the specifics of each subject area characterizing an energy facility to obtain reliable forecast results, power consumption modeling remains an urgent task despite a wide variety of other methods. The review was conducted with an assessment of the methods according to the following criteria: labor intensity, requirements for the initial data set, scope of application, accuracy of the forecasting method, the possibility of application for other forecasting horizons. The above classification of methods according to the anticipation period allows highlights the fact that when predicting power consumption for different time intervals, the same methods are often used. Therefore, it is worth emphasizing the importance of classifying the forecast over the forecasting horizon not to differentiate the methods used to predict electricity consumption for each period but to consider the specifics of each type of forecasting (operative, short-term, medium-term, long-term).

8 citations

Journal ArticleDOI
TL;DR: In this paper , the authors performed an overall analysis of energy consumption in smart homes by deploying machine learning models, such as Decision Trees (DT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and k-Nearest Neighbor (KNN) for predicting the power consumption of multiple datasets.
Abstract: Smart homes incorporate several devices that automate tasks and make our lives easy. These devices can be useful for many things, like security access, lighting, temperature, etc. Using the Internet of Things (IoT) platform, smart homes essentially let homeowners control appliances and devices remotely. Due to their self-learning skills, smart homes can learn homeowners' schedules and adapt accordingly to make adjustments. Since convenience and cost savings is necessary in such an environment, and there are multiple devices involved, there is a need to analyze power consumption in smart homes. Moreover, increased energy consumption leads to an increase in carbon footprint, elevates the risk of climate, and leads to increased demand in supply. Hence, monitoring energy consumption is crucial. In this paper, we perform an overall analysis of energy consumption in smart homes by deploying machine learning models. We rely on machine learning techniques, like Decision Trees (DT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and k-Nearest Neighbor (KNN) for predicting the power consumption of multiple datasets. We also propose a DT-RF-XGBoost-based Ensemble Model for analyzing the consumption and comparing it with the baseline algorithms. The evaluation parameters used in the study are Mean Square Error (MSE), R-squared (R 2, ), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), respectively. The study has been performed on multiple datasets and our study shows that the proposed DT-RF-XG-based Ensemble Model outperforms all the other baseline algorithms for multiple datasets with R 2 around 0.99.

6 citations

References
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Book ChapterDOI
01 Jan 2010
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5,561 citations

Journal ArticleDOI
TL;DR: The proposed LSTM approach outperforms the other listed rival algorithms in the task of short-term load forecasting for individual residential households and is comprehensively compared to various benchmarks including the state-of-the-arts in the field of load forecasting.
Abstract: As the power system is facing a transition toward a more intelligent, flexible, and interactive system with higher penetration of renewable energy generation, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly essential role in the future grid planning and operation. Other than aggregated residential load in a large scale, forecasting an electric load of a single energy user is fairly challenging due to the high volatility and uncertainty involved. In this paper, we propose a long short-term memory (LSTM) recurrent neural network-based framework, which is the latest and one of the most popular techniques of deep learning, to tackle this tricky issue. The proposed framework is tested on a publicly available set of real residential smart meter data, of which the performance is comprehensively compared to various benchmarks including the state-of-the-arts in the field of load forecasting. As a result, the proposed LSTM approach outperforms the other listed rival algorithms in the task of short-term load forecasting for individual residential households.

1,415 citations

Journal ArticleDOI
TL;DR: A global technique for averaging a set of sequences is developed, which avoids using iterative pairwise averaging and is thus insensitive to ordering effects, and a new strategy to reduce the length of the resulting average sequence is described.

823 citations

Journal ArticleDOI
TL;DR: The concept of cumulant and bispectrum are embedded into the ARMA model in order to facilitate Gaussian and non-Gaussian process considerations, and with embodiment of a Gaussianity verification procedure, the forecasted model is identified more appropriately.
Abstract: In this paper, the short-term load forecast by use of autoregressive moving average (ARMA) model including non-Gaussian process considerations is proposed. In the proposed method, the concept of cumulant and bispectrum are embedded into the ARMA model in order to facilitate Gaussian and non-Gaussian process. With embodiment of a Gaussianity verification procedure, the forecasted model is identified more appropriately. Therefore, the performance of ARMA model is better ensured, improving the load forecast accuracy significantly. The proposed method has been applied on a practical system and the results are compared with other published techniques.

597 citations

Journal ArticleDOI
TL;DR: An overview of the state of the art and recent developments enabling higher intelligence in future smart grids is provided and the integration of renewable sources and storage systems into the power grids is analyzed.
Abstract: Renewable energy sources are one key enabler to decrease greenhouse gas emissions and to cope with the anthropogenic climate change. Their intermittent behavior and limited storage capabilities present a new challenge to power system operators to maintain power quality and reliability. Additional technical complexity arises from the large number of small distributed generation units and their allocation within the power system. Market liberalization and changing regulatory framework lead to additional organizational complexity. As a result, the design and operation of the future electric energy system have to be redefined. Sophisticated information and communication architectures, automation concepts, and control approaches are necessary in order to manage the higher complexity of so-called smart grids. This paper provides an overview of the state of the art and recent developments enabling higher intelligence in future smart grids. The integration of renewable sources and storage systems into the power grids is analyzed. Energy management and demand response methods and important automation paradigms and domain standards are also reviewed.

463 citations