•GI-MARCOS, decision making model for building energy demand analysis is presented.•Gini index assigns objective weight to attributes in each phase of implementation.•MARCOS provides reliable ranking ...of alternatives based on compromise solution.•Case study validation in terms of correlation & sensitivity analysis is presented.
With the existence of several conventional and advanced building thermal energy demand forecast models to improve the energy efficiency of buildings, it is hard to find an appropriate, convenient, and efficient model. Evaluations based on statistical indexes (MAE, RMSE, MAPE, etc.) that characterize the accuracy of the forecasts do not help in the identification of the efficient building thermal energy demand forecast tool since they do not reflect the efforts entailed in implementation of the forecast model, i.e., data collection to production/use phase. Hence, this work presents a Gini Index based Measurement of Alternatives and Ranking according to COmpromise Solution (GI-MARCOS), a hybrid Multi Attribute Decision Making (MADM) approach for the identification of the most efficient building energy demand forecast tool. GI-MARCOS employs (i) GI based objective weight method: assigns meaningful objective weights to the attributes in four phases (1: pre-processing, 2: implementation, 3: post-processing, and 4: use phase) thereby avoiding unnecessary biases in the expert's opinion on weights and applicable to domains where there is a lack of domain expertise, and (ii) MARCOS: provides a robust and reliable ranking of alternatives in a dynamic environment. A case study with three alternatives evaluated over three to six attributes in four phases of implementation (pre-processing, implementation, post-processing and use) reveals that the use of GI-MARCOS improved the accuracy of alternatives MLR and BM by 6% and 13%, respectively. Moreover, additional validations state that (i) MLR performs best in Phase 1 and 2, while ANN performs best in Phase 3 and 4 with BM providing a mediocre performance in all four phases, (ii) sensitivity analysis: provides robust ranking with interchange of weights across phases and attributes, and (iii) rank correlation: ranks produce by GI-MARCOS has a high correlation with GRA (0.999), COPRAS (0.9786), and ARAS (0.9775).
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In a cloud marketplace, the existence of wide range of Cloud Service Providers (CSPs) makes it hard for the Cloud Users (CUs) to find an appropriate CSP based on their requirements. The design of a ...suitable service selection framework helps the users in the selection of a suitable CSP, while motivating the CSPs to satisfy the assured Service Level Agreement (SLA) and enhance the Quality of Service (QoS). Existing service selection models employ random assignment of weights to the QoS attributes, replacement of missing data by random values, etc. which results in an inaccurate ranking of the CSPs. Moreover, these models have high computational overhead. In this study, a novel cloud service selection architecture, Hypergraph based Computational Model (HGCM) and Minimum Distance-Helly Property (MDHP) algorithm have been proposed for ranking the cloud service providers. Helly property of the hypergraph had been used to assign weights to the attributes and reduce the complexity of the ranking model, while arithmetic residue and Expectation–Maximization (EM) algorithms were used to impute missing values. Experimental results provided by MDHP under different case studies (dataset used by various research communities and synthetic dataset) confirms the ranking algorithm to be scalable and computationally attractive.
•This paper presents a Hypergraph based Computational Model (HGCM) for ranking CSPs.•HGCM uses hypergraph based technique (Minimum Distance-Helly Property (MDHP)) to evaluate the CSPs.•MDHP exploits the relation between the Service Measurement Index (SMI) metrics to select appropriate CSPs based on CUs requirement.•HGCM uses arithmetic residue and Expectation–Maximization (EM) algorithms to impute missing values.•Complexity of HGCM has been reduced through exploitation of the Helly property.
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‘Curse of Dimensionality’ and the trade-off between high detection rate and less false alarm rate make the design of an efficient and robust Intrusion Detection System, an open research challenge. In ...this way, we present Hyper Clique—Improved Binary Gravitational Search Algorithm based Support Vector Machine (HC-IBGSA SVM), an efficient and adaptive intrusion detection technique to improve the performance of SVM in terms of detection rate and false alarm rate. HC-IBGSA SVM employs hyper clique property of hypergraph, novel mutation operator, and Newton–Raphson inspired position update function to fasten the search for an optimal solution and to prevent premature convergence. Further, HC-IBGSA uses a weighted objective function to maintain the trade-off between maximizing detection rate and minimizing the false alarm rate and the optimal number of features. The experimental evaluations were carried out using two benchmark intrusion datasets, namely NSL-KDD CUP dataset and UNSW-NB15 dataset under two scenarios (1) SVM trained with all features, and (2) SVM trained with the optimal feature subset and model parameters obtained from HC-IBGSA in terms of various quality metrics, stability analysis and statistical test.
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CEKLJ, EMUNI, FZAB, GEOZS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Increasing global building energy demand, with the related economic and environmental impact, upsurges the need for the design of reliable energy demand forecast models. This work presents kCNN-LSTM, ...a deep learning framework that operates on the energy consumption data recorded at predefined intervals to provide accurate building energy consumption forecasts. kCNN-LSTM employs (i) k−means clustering – to perform cluster analysis to understand the energy consumption pattern/trend; (ii) Convolutional Neural Networks (CNN) – to extract complex features with non-linear interactions that affect energy consumption; and (iii) Long Short Term Memory (LSTM) neural networks – to handle long-term dependencies through modeling temporal information in the time series data. The efficiency and applicability of kCNN-LSTM were demonstrated using a real time building energy consumption data acquired from a four-storeyed building in IIT-Bombay, India. The performance of kCNN-LSTM was compared with the k-means variant of the state-of-the-art energy demand forecast models in terms of well-known quality metrics. It is also observed that the accurate energy demand forecast provided by kCNN-LSTM due to its ability to learn the spatio-temporal dependencies in the energy consumption data makes it a suitable deep learning model for energy consumption forecast problems.
•kCNN-LSTM, a deep learning based energy consumption forecast model, is presented.•Timestamp based features generated from the timestamp provides parameter-rich data.•k-means clustering analyzes the trend in energy consumption data.•CNN-LSTM captures spatio-temporal characteristics in energy consumption data.•A case study on KReSIT building energy consumption data is presented.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•eDemand, a data driven energy consumption forecasting model is developed.•The proposed model is designed for accurate short, mid, and long term forecasting.•Improved version of SCOA is used to ...identify the optimal hyperparameters of LSTM.•A case study using KReSIT power consumption data is presented.•Impact of hidden layers, hidden units and dropout on model accuracy was analysed.
Data driven building energy consumption forecasting models play a significant role in enhancing the energy efficiency of the buildings through building energy management, energy operations, and control strategies. The multi-source and heterogeneous energy consumption data necessitates the integration of evolutionary algorithms and data-driven models for better forecast accuracy and robustness. We present eDemand, an energy consumption forecasting model which employs long short term memory networks and improved sine cosine optimization algorithm for accurate and robust building energy consumption forecasting. A novel Haar wavelet based mutation operator was introduced to enhance the divergence nature of sine cosine optimization algorithm towards the global optimal solution. Further, the hyperparameters (learning rate, weight decay, momentum, and number of hidden layers) of the LSTM were optimized using the improved sine cosine optimization algorithm. A case study on the real-time energy consumption data obtained from Kanwal Rekhi building, an academic building at Indian Institute of Technology, Bombay for short, mid, and long-term forecasting. Experiments reveal that the proposed model outperforms the state-of-the-art energy consumption forecast models in terms of mean absolute error, mean absolute percentage error, mean square error, root mean square error, and Theil statistics. It is shown that stable and accurate forecast results are produced by ISCOA-LSTM and hence it can be used as an efficient tool for solving energy consumption forecast problems.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Since the thermal condition of living spaces affects the occupants' productivity and their quality of life, it is important to design effective heating, ventilation and air conditioning (HVAC) ...control strategies for better energy efficiency and thermal comfort. An essential step in HVAC control and energy optimization is thermal comfort modeling. Recently, data-driven thermal comfort models have been preferred over the Fanger's Predicted Mean Vote (PMV) model due to higher accuracy and ease of use. However, the unavailability of comprehensive labelled thermal comfort data from the occupants poses a significant modeling challenge. This paper addresses data inadequacy issues by adopting ‘transfer learning’ to leverage well learned knowledge from source domain (same climate zones) to target domain (different climate zone) where modeling data is sparse. Specifically, a Transfer Learning based Convolutional Neural Networks-Long Short Term Memory neural networks (TL CNN-LSTM) is designed for effective thermal comfort modeling that exploits the spatio-temporal relations in the thermal comfort data. The significant modeling parameters for TL CNN-LSTM are identified using the Chi-squared test. Further, the lack of sufficient samples across all thermal conditions in the available thermal comfort datasets was handled by Synthetic Minority Oversampling Technique (SMOTE). Experiments with two source (ASHRAE RP-884 and Scales Project) and one target (Medium US office) datasets demonstrate the ability of TL CNN-LSTM in achieving an accuracy of >55% with limited data in target buildings. The limitation of TL CNN-LSTM is its continued dependence on intrusive parameters and the challenges in assessing its adaptability to different climate zones.
•Transfer learning based CNN-LSTM model is presented for thermal comfort modeling.•It provides accurate thermal comfort prediction for buildings with limited data.•Significant thermal comfort parameters are identified using Chi-square statistics.•SMOTE oversampling technique is used to address the class imbalance problem.•Impact of different thermal comfort parameters on the accuracy of model is studied.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Cloud service provider selection, an important Multi-Criteria Decision Making (MCDM) problem involves intrinsic relations among the multiple alternatives, attributes and decision experts for the ...selection of trustworthy Cloud Service Providers (CSPs). Due to uncertain and incomplete nature of the cloud service provider evaluation data, i.e., Quality of Service (QoS) and user feedbacks, the identification of suitable CSPs with accurate service ranking remains an open research challenge. To address the above-mentioned challenge, this work presents an Improved Interval-Valued Intuitionistic Fuzzy Sets-Weighted Aggregate Sum and Product Assessment (IIVIFS-WASPAS) based cloud service provider selection approach for the identification of Trustworthy CSPs (TCSPs). The proposed CSP selection approach employs integrated objective and subjective weight assessment method and IVIFS-WASPAS method to determine the importance of QoS attributes and to rank the TCSPs respectively. Further, a novel preference-attitudinal score and accuracy function of IIVIFS have been designed based on the decision maker’s attitude to rank the TCSPs. Case studies using Cloud Armor, a real-world trust feedback dataset demonstrates the accuracy, effectiveness, and feasibility of IIVIFS-WASPAS approach for CSP selection problem in terms of sensitivity analysis and Rank Reversal Phenomenon (RRP).
•IIVIFSs based cloud service selection approach is presented.•Integrated weight assessment approach to identify the weights of the QoS attributes.•Preference attitudinal score and accuracy functions for accurate service ranking.•Validations using case studies on a sample trust feedback dataset from Cloud Armor.•IIVIFSs validated in terms of sensitivity analysis and rank reversal phenomenon.
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Advancements in Internet of Energy (IoE) technologies drive the development of several energy efficient frameworks for better energy optimization, economic savings, safety, and security in smart ...homes. However, certain challenges such as real-time operational data for each micro-moment, proper application of data fusion techniques, and end-to-end computing and deployment architecture prevent the establishment of an effective energy-efficient framework to provide personalized energy-saving recommendations. This work presents energy management for smart spaces (EMSS), the proposed energy efficiency framework implemented in an edge-cloud computing platform that fuses data from heterogeneous data sources (environmental sensors, camera, plug data, etc.) at appropriate data fusion levels and process them to generate actionable, explainable, personalized, and persuasive recommendations at the right moment. The user response to the generated recommendations triggers the actuators to perform respective energy-saving actions and provide more personalized future recommendations. Further, SMARTHome - a data generation framework based on configurable scenario files and a set of software codes was proposed to generate synthetic data with respect to different building types and micro-moments. The functionalities of the EMSS (device and user registration), user dashboard, analytics, and energy-saving recommendations were made accessible to the user through web and mobile applications. The validation analysis of the EMSS was performed by (i) comparative analysis of the machine learning and deep learning algorithms used by the decision engine to generate energy-saving recommendations and (ii) benchmarking of EMSS based on the taxonomy of data fusion-based energy efficiency frameworks for smart homes.
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•EMSS, an end-to-end energy efficiency framework for smart spaces.•SMARTHome, data generation framework to generate synthetic dataset.•Smart data fusion approach to apply different levels of data fusion.•Actionable, explainable, persuasive, and personalized energy-saving recommendations.•Implemented on an edge-cloud computing platform.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Realization of the importance for advanced tool and techniques to secure the network infrastructure from the security risks has led to the development of many machine learning based intrusion ...detection techniques. However, the benefits and limitations of these techniques make the development of an efficient Intrusion Detection System (IDS), an open challenge. This paper presents an adaptive, and a robust intrusion detection technique using Hypergraph based Genetic Algorithm (HG - GA) for parameter setting and feature selection in Support Vector Machine (SVM). Hyper – clique property of Hypergraph was exploited for the generation of initial population to fasten the search for the optimal solution and to prevent the trap at the local minima. HG-GA uses a weighted objective function to maintain the trade-off between maximizing the detection rate and minimizing the false alarm rate, along with the optimal number of features. The performance of HG-GA SVM was evaluated using NSL-KDD intrusion dataset under two scenarios (i) All features and (ii) informative features obtained from HG – GA. Experimental results show the prominence of HG-GA SVM over the existing techniques in terms of classifier accuracy, detection rate, false alarm rate, and runtime analysis.
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