The transactive energy market is an emerging development in energy economics built on advanced metering infrastructure. Data generated in this context is often required for market operations, while also being privacy sensitive. This dual concern has necessitated the development of various methods of obfuscation in order to maintain privacy while still facilitating operations. While data aggregation is a common approach in this context, many of the existing aggregation methods rely on additional network components or lack flexibility. In this paper, we introduce Cyclic Homomorphic Encryption Aggregation (CHEA), a secure aggregation protocol that eliminates the need for additional network components or complicated key distribution schemes, while providing additional capabilities compared to similar protocols. We validate our scheme with formal security analysis as well as a software simulation of a transactive energy network running the scheme. Results indicate that CHEA performs well in comparison to similar works, with minimal communication overheads. Additionally, CHEA retains all standard security properties held by other aggregation schemes, while improving flexibility and reducing infrastructural requirements. Our scheme operates on similar assumptions as other works, but current smart metering hardware lags in terms of processing power, making the scheme infeasible on the current generation of hardware. However, these capabilities should quickly advance to an accommodating state. With this in mind, and given the results, we believe CHEA is a strong candidate for aggregating transactive energy data.

The vagueness in human opinions, the randomness in the interactions among individuals, and the nonlinearities in opinion updates are challenging issues in modeling opinion formation and consensus. In this paper, we address the theoretical foundation and the necessary conditions for the convergence of opinion formation's nonlinear dynamics to the true states. For this purpose, we extend a probabilistic fuzzy model of opinion formation to include the effect of perceiving random and vague media content on the uncertainty of individuals' opinions. In this model, each individual faces an uncertain media that is statistically modeled. Hence the individual perceives a probabilistic fuzzy set of possible environmental states and shapes its probabilistic fuzzy opinion (belief) based on its perceptions and negotiations. Theoretical results reveal that the media contents should be statistically supportive of the true state and that the coefficients in the belief update algorithms should be selected within the extracted bounds for the perception aggregation process and the proposed optimistic and pessimistic assuring operators. Application to two networks with different sizes of 94 and 918 nodes with 1134 and 206869 edges, respectively, confirms these theoretical conclusions. These simulation results show that higher diversity is observed in the opinions of the highly connected individuals. Furthermore, convergence is delayed for the larger network, i.e. there remain few individuals with differing opinions until the late stages of convergence. It is also shown that model convergence to the true state may deteriorate if parameter settings do not adhere to the theoretically obtained bounds.

In recent years, the emerging significance of coordination of distributed energy resources has established a pathway toward developing distributed control frameworks for power systems. In this regard, the role of communication bottlenecks in the performance of distributed agents (for applications such as Distributed Optimal Power Flow and Transactive Based Grid Control) needs to be studied in more detail. In this paper, a new version of alternating direction multiplier method (ADMM) is implemented, where the D-OPF agents apply predictive approaches to estimate upcoming agents’ consensus messages about D-OPF global variables in case of communication links disruptions. The IEEE 14-bus network is used to solve the distributed optimal power flow (DOPF) in three scenarios using the developed extended ADMM method to compensate for lost/interrupted agents’ consensus messages. The OPF results show that by using the developed estimation algorithm for the D-OPF agents, in the case of interrupted communication links, the global variables converge faster to their actual values and the D-OPF results are feasible.

This paper proposes a topology reconfiguration approach for improving the rate of convergence of opinions to the opinion of a pre-specified leader for the class of convergent Deffuant models with a limited number of links. From a systems theory perspective, this problem can be viewed as a constrained stochastic nonlinear on–off control problem. Accordingly, we first propose a deterministic version of the Deffuant model and rewrite its dynamic equations to reach a set of nonlinear state-space equations where opinions are state variables and link connectivities are inputs. For that model, we then design an on–off controller based on a short-sighted predictive control strategy that dynamically changes the topology of the network by a low computational burden process. Results confirm that the proposed control strategy reaches faster convergence rates of opinions to the leader’s opinion in comparison with the well-known Erdős–Rényi structure with a similar number of links. The proposed control strategy also provides a higher rate of link connectivity for the links that are connected to the leader. Furthermore, it is observed that if the network has a fixed topology based on the obtained rate of link connectivity, it will still have a relatively rapid convergence rate which is comparable with that of a fully connected topology.

Through transactive energy (TE) platforms, prosumers can enter into a contractual agreement with an Independent Electricity System Operator (IESO) to buy and sell energy. Accordingly, the TE contract holders are liable for contractual violations. Manual compliance checking of such transactions is infeasible due to large number of market rules as well as the plethora of executing TE contracts. Moreover, the TE system big data (e.g., offers, bids, and transaction activities) need to be maintained on a transparent, reliable, and secure plat-form. This paper presents a compliance checking method for transactive energy markets based on the IESO (in Ontario, Canada) market rules by using smart contracts that assure the integrity, reliability, and transparency of energy transactions’ data with a permissioned blockchain. The performance of the blockchain network is evaluated through transaction latency and resource utilization. In addition, an acceptance test is successfully conducted to validate the correctness of the platform in terms of trading workflow, runtime status of the TE contracts, and the quality of market clearing results.

Building energy simulation is traditionally applied at late phases of the design, when most decisions on specifications of building systems are made. There has been a growing attempt in the literature to enable such simulations at earlier phases of design, when decisions' impact on the lifecycle behavior is the strongest. While researchers have developed tools/models to support planning and conceptualization phases from this aspect; there have been not much developments for early design development stage (which is usually the scope of semi-detailed cost estimates). This paper introduces a software tool for generating multiple design scenarios and evaluating them, by combining energy simulation and economic analyses, at schematic design and early design development phases of building projects. In this regard, we used state-of-the art of open source technologies, as well as cloud computing, to integrate energy simulation into the alternative selection procedure. The developed system focuses on architectural parameters; lighting power density; and heating, ventilation and air conditioning (HVAC). Analysis of sensitivity of lifecycle energy to such parameters, in a group of representative real-world projects, helped us to limit variations of such parameters, and subsequently their combinations. Using OpenStudio measures and the computational power offered by the cloud, our system generates/simulates all possible design scenarios (combinations of alternative variations for design parameters); and compares them based on building economics performance measures. The workflow is showcased in a case study project, by automatically creating and evaluating 97 design scenarios, based on economic efficiency and liquidity.

Accurate day-ahead (or 24-hours ahead) electric load forecasting for power systems is crucial for system's optimal operations. In evolving smart distribution grids, the importance of precise electric load forecast in day-ahead is even more important for distributed energy management systems (DERMS) and demand response (DR) programs, which are used by the independent system operators (ISO) and power utilities (PU) for day-ahead system planning and optimal operations. This paper captures both dynamic members' interdependencies and the impact of meteorological factors on the load sequence. In this regard, Long Short-Term Memory (LSTM) is applied to use the historical load sequences to forecast the 24 hours ahead values of the system load. On the other hand, a Deep Feedforward Neural Network (DFNN) is applied to map the forecasted meteorological parameters to the upcoming 24-hourly values of the system load. Finally, based on the historical errors of these two engines, a Beta distribution generates a probabilistic weight for aggregating the forecasted values at each hour. The proposed forecasting model performs better than single engines based on Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) metric when applied to day-ahead load forecasting for the city of Toronto.

Opinion formation in social networks is an interesting dynamical process from the perspective of system modeling due to its large scale as well as the variety of structural and parametric uncertainties that it entails. This paper proposes a probabilistic fuzzy opinion formation model for predicting the opinions of communities in the social networks. In this regard, the opinions of a group of individuals about a given topic in a Telegram pilot group, as a popular social network, are collected and presented in the framework of the probabilistic fuzzy model. Based on the obtained data, the parameters of the model are extracted, and the model is tuned. Finally, the variations of the actual opinions throughout time are compared with the model predictions. The numerical results in this study show that, with appropriately tuned parameters, the model successfully represents the opinion formation process, with an average error that approaches zero.

Although photovoltaic arrays are a highly reliable solution to generating energy, the full scale use of these integrated arrays presents formidable challenges since their future generation carries high uncertainty. In this paper, the success of a large-scale solar farm in generating different percentages of its committed value is forecasted by a new opinion formation model. In the proposed model, each node uses local generation data, transforms them to a fuzzy set called perception, and evolves its fuzzy opinion about the entire farm's future generation by using this perception and negotiating with other nodes. As a result, the fuzzy predictions, which can be used in fuzzy energy management systems, are achieved with simple, distributed, and scalable computations. As a case study, Belgium is considered here as the entire solar farm with its provinces as the local generation units. To evaluate the predicted fuzzy opinions, we extract the lower/upper and point-wise predictions from them and compare these extracted values with the outputs of central data-driven forecasting algorithms. Furthermore, to evaluate the proposed aggregation mechanism, the obtained opinions are compared with the fusion of perceptions by conventional fusion algorithms. The results show that in both experiments, the proposed strategy performs better than its competitors in the sense of maximum absolute percentage error and mean-squared error.

The removal of stopwords is an important preprocessing step in many natural language processing tasks, which can lead to enhanced performance and execution time. Many existing methods either rely on a predefined list of stopwords or compute word significance based on metrics such as tf-idf. The objective of our work in this paper is to identify stopwords, in an unsupervised way, for streaming textual corpora such as Twitter, which have a temporal nature. We propose to consider and model the dynamics of a word within the streaming corpus to identify the ones that are less likely to be informative or discriminative. Our work is based on the discrete wavelet transform (DWT) of word signals in order to extract two features, namely scale and energy. We show that our proposed approach is effective in identifying stopwords and improves the quality of topics in the task of topic detection.

Accurate price forecasting is crucial for all market participants in electricity markets. This study presents a hybrid price forecasting framework based on a new data fusion algorithm. Owing to the complexity and distinct nature of the electricity price, a single forecast engine cannot capture all different patterns of the price signals. Hence, this study focuses on a hybrid forecasting method to extract the advantages of several forecasting engines. In the proposed method, artificial neural network, adaptive neuro-fuzzy inference system and autoregressive moving average are employed as primary forecast engines (agents) which provide three independent price forecasts. Then, a new data fusion algorithm, the modified ordered weighted average (modified OWA), is proposed to combine the three forecasts to generate a single unified price forecast. Hopefully, the fusion's output outperforms all the agents' forecasts. The author's proposed fusion algorithm, unlike conventional OWA, uses the feedback from the agents' error. The proposed framework is evaluated on the Spanish electricity market. The results confirm the ability of the proposed fusion framework to provide more accurate forecasts compared with the input agents forecasts. Results are also compared with some of the recent electricity price forecasting methods.

In this paper, a recently developed theorem in the field of stability of interval valued type 2 fuzzy controllers has been studied and a new adaptive control strategy has been proposed due to this theorem. Mentioned theorem, present some constrains for the stability of the system which are dependent on the upper and lower bounds of membership function of the IVT2 model. These bounds are dependent on the parametric uncertainties in the plant. In this paper, it is proposed to use recursive least square algorithm to identify unknown parameters to narrow footprint of uncertainties in the membership functions of IVT2 model. Narrowing FOU through the time, studied theorem presents more relaxed constrains as a result of which, space of stabilizing controllers would be extended. Searching in the extended space, a controller with a better performance could be selected using genetic algorithm. Proposed algorithm is applied on uncertain model of inverted pendulum and results show that disturbances which are modeled in the format of initial conditions could be rejected faster.

In this paper, Kalman Fusion algorithm is applied to combine outputs of three forecasting engines which are used to predict electricity price signal of the Spanish electricity market. Employed engines which are Adaptive Neuro-fuzzy Inference System (ANFIS), Artificial Neural Networks (ANN) and Autoregressive Moving Average (ARMA), are all powerful and popular kinds of time series models. After applying these algorithms on the preprocessed data of the Spanish electricity market, outputs of the aforementioned models are fused by Kalman fusion algorithm in order to exploit the advantages of these forecasting engines simultaneously, as a result of which different patterns existing among price time series can be forecasted more accurately. In comparison with single forecasting methods utilized in this paper to forecast electricity price signal, results of the proposed model based on Kalman Fusion algorithm prove that this approach in effective to enhance accuracy of prediction.

This paper presents a new information based method of prediction which helps to obtain more information from a little data set and applies it on a data set of photovoltaic power plant of University of Tehran which only contained data of 100 days at the time of research. In this way some conventional models of time series are applied on the data set and output of models are fused by some common fusion algorithms and a new fusion algorithm which is proposed in this paper. Results show that using fusion algorithm a better forecasting could be obtained in the sense of maximum error of forecasting, root mean square of error, similarity in forecasting pattern of power generation in cloudy days and finally statistical characteristics of residuals. Results also express proposed algorithm could be observed as a good fusion algorithm.

This paper presents a dynamic non-linear model of a micro-grid and then applies the GA algorithm to optimally manage the short-term operation of the studied micro-grid. The original calculus of variations method has been modified and augmented with GA-algorithm to solve non-linear optimal control problems, such as the optimal short-term operation of a micro-grid with non-linear dynamics. To validate the proposed dynamic model of the selected micro-grid and to evaluate the accuracy and performance of the developed GA-based optimization algorithm a simulation case study is presented and the obtained results are analyzed and compared with the simplified LQR problem using the Lagrange Multipliers (LM) theory(where the nonlinearity of the micro-grid model is ignored). The simulation results clearly show the superiority of the proposed method in this paper versus the original LQR modeling and optimization using the LM theory.