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- Demonstration of an Energy Consumption Forecasting System for Energy Management in BuildingsPublication . Jozi, Aria; Ramos, Daniel; Gomes, Luis; Faria, Pedro; Pinto, Tiago; Vale, ZitaDue to the increment of the energy consumption and dependency of the nowadays lifestyle to the electrical appliances, the essential role of an energy management system in the buildings is realized more than ever. With this motivation, predicting energy consumption is very relevant to support the energy management in buildings. In this paper, the use of an energy management system supported by forecasting models applied to energy consumption prediction is demonstrated. The real-time automatic forecasting system is running separately but integrated with the existing SCADA system. Nine different forecasting approaches to obtain the most reliable estimated energy consumption of the building during the following hours are implemented.
- Electricity consumption forecasting in office buildings: an artificial intelligence approachPublication . Jozi, Aria; Pinto, Tiago; Marreiros, Goreti; Vale, ZitaThe rising needs for increased energy efficiency and better use of renewable energy sources bring out the necessity for improved energy management and forecasting models. Electricity consumption, in particular, is subject to large variations due to the effect of multiple variables, such as the temperature, luminosity or humidity, and of course, consumers' habits. Current forecasting models are not able to deal adequately with the influence and correlation between the multiple involved variables. Hence, novel, adaptive forecasting models are needed. This paper presents a novel approach based on multiple artificial intelligence-based forecasting algorithms. The considered algorithms are artificial neural networks, support vector machines hybrid fuzzy inference systems, Wang and Mendel's fuzzy rule learning method and a genetic fuzzy system for fuzzy rule learning based on the MOGUL methodology. These algorithms are used to forecast the electricity consumption of a real office building, using multiple input variables and consumption disaggregation.
- Energy flexibility assessment of a multi agent-based smart home energy systemPublication . Gazafroudi, Amin Shokri; Pinto, Tiago; Prieto-Castrillo, Francisco; Corchado, Juan Manuel; Abrishambaf, Omid; Jozi, Aria; Vale, ZitaPower systems worldwide are complex and challenging environments. The increasing necessity for an adequate integration of renewable energy sources is resulting in a rising complexity in power systems operation. Multi-agent based simulation platforms have proven to be a good option to study the several issues related to these systems. In a smaller scale, a home energy management system would be effective for the both sides of the network. It can reduce the electricity costs of the demand side, and it can assist to relieve the grid congestion in peak times. This paper represents a domestic energy management system as part of a multi-agent system that models the smart home energy system. Our proposed system consists of energy management and predictor systems. This way, homes are able to transact with the local electricity market according to the energy flexibility that is provided by the electric vehicle, and it can manage produced electrical energy of the photovoltaic system inside of the home.
- Electricity consumption forecasting in office buildings: an artificial intelligence approachPublication . Jozi, Aria; Pinto, Tiago; Marreiros, Goreti; Vale, ZitaThe rising needs for increased energy efficiency and better use of renewable energy sources bring out the necessity for improved energy management and forecasting models. Electricity consumption, in particular, is subject to large variations due to the effect of multiple variables, such as the temperature, luminosity or humidity, and of course, consumers' habits. Current forecasting models are not able to deal adequately with the influence and correlation between the multiple involved variables. Hence, novel, adaptive forecasting models are needed. This paper presents a novel approach based on multiple artificial intelligence-based forecasting algorithms. The considered algorithms are artificial neural networks, support vector machines hybrid fuzzy inference systems, Wang and Mendel's fuzzy rule learning method and a genetic fuzzy system for fuzzy rule learning based on the MOGUL methodology. These algorithms are used to forecast the electricity consumption of a real office building, using multiple input variables and consumption disaggregation.
- Day ahead electricity consumption forecasting with MOGUL learning modelPublication . Jozi, Aria; Pinto, Tiago; Praça, Isabel; Vale, Zita; Soares, JoãoDue to amount of today's electricity consumption, one of the most important tasks of the energy operators is to be able to predict the consumption and be ready to control the energy generation based on the estimated consumption for the future. In this way, having a trustable forecast of the electricity consumption is essential to control the consumption and maintain the balance in energy distribution networks. This study presents a day ahead forecasting approach based on a genetic fuzzy system for fuzzy rule learning based on the MOGUL methodology (GFS.FR.MOGUL). The proposed approach is used to forecast the electricity consumption of an office building in the following 24 hours. The goal of this work is to present a more reliable profile of the electricity consumption comparing to previous works. Therefore, this paper also includes the comparison of the results of day ahead forecasting using GFS.FR.MOGUL method against other fuzzy rule based methods, as well as a set of Artificial Neural Network(ANN) approaches. This comparison shows that using the GFS.FR.MOGUL forecasting method for day-ahead electricity consumption forecasting is able to estimate a more trustable value than the other approaches.
- Economic Evaluation of Predictive Dispatch Model in MAS-Based Smart HomePublication . Gazafroudi, Amin Shokri; Prieto-Castrillo, Francisco; Pinto, Tiago; Jozi, Aria; Vale, ZitaThis paper proposes a Predictive Dispatch System (PDS) as part of a Multi-Agent system that models the Smart Home Electricity System (MASHES). The proposed PDS consists of a Decision-Making System (DMS) and a Prediction Engine (PE). The considered Smart Home Electricity System (SHES) consists of different agents, each with different tasks in the system. A Modified Stochastic Predicted Bands (MSPB) interval optimization method is used to model the uncertainty in the Home Energy Management (HEM) problem. Moreover, the proposed method to solve HEM problem is based on the Moving Window Algorithm (MWA). The performance of the proposed Home Energy Management System (HEMS) is evaluated using a JADE implementation of the MASHES.
- Decision Support Application for Energy Consumption ForecastingPublication . Jozi, Aria; Pinto, Tiago; Praça, Isabel; Vale, ZitaEnergy consumption forecasting is crucial in current and future power and energy systems. With the increasing penetration of renewable energy sources, with high associated uncertainty due to the dependence on natural conditions (such as wind speed or solar intensity), the need to balance the fluctuation of generation with the flexibility from the consumer side increases considerably. In this way, significant work has been done on the development of energy consumption forecasting methods, able to deal with different forecasting circumstances, e.g., the prediction time horizon, the available data, the frequency of data, or even the quality of data measurements. The main conclusion is that different methods are more suitable for different prediction circumstances, and no method can outperform all others in all situations (no-free-lunch theorem). This paper proposes a novel application, developed in the scope of the SIMOCE project (ANI|P2020 17690), which brings together several of the most relevant forecasting methods in this domain, namely artificial neural networks, support vector machines, and several methods based on fuzzy rule-based systems, with the objective of providing decision support for energy consumption forecasting, regardless of the prediction conditions. For this, the application also includes several data management strategies that enable training of the forecasting methods depending on the available data. Results show that by this application, users are endowed with the means to automatically refine and train different forecasting methods for energy consumption prediction. These methods show different performance levels depending on the prediction conditions, hence, using the proposed approach, users always have access to the most adequate methods in each situation
- Energy consumption forecasting using genetic fuzzy rule-based systems based on MOGUL learning methodologyPublication . Jozi, Aria; Pinto, Tiago; Praça, Isabel; Silva, Francisco; Teixeira, Brígida; Vale, ZitaOne of the most challenging tasks for energy domain stakeholders is to have a better preview of the electricity consumption. Having a more trustable expectation of electricity consumption can help minimizing the cost of electricity and also enable a better control on the electricity tariff. This paper presents a study using a Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative rule Learning approach (MOGUL) methodology in order to have a better profile of the electricity consumption of the following hours. The proposed approach uses the electricity consumption of the past hours to forecast the consumption value for the following hours. Results from this study are compared to those of previous approaches, namely two fuzzy based systems: and several different approaches based on artificial neural networks. The comparison of the achieved results with those achieved by the previous approaches shows that this approach can calculate a more reliable value for the electricity consumption in the following hours, as it is able to achieve lower forecasting errors, and a less standard deviation of the forecasting error results
- Day-ahead forecasting approach for energy consumption of an office building using support vector machinesPublication . Jozi, Aria; Pinto, Tiago; Praça, Isabel; Vale, ZitaThis paper presents a Support Vector Machine (SVM) based approach for energy consumption forecasting. The proposed approach includes the combination of both the historic log of past consumption data and the history of contextual information. By combining variables that influence the electrical energy consumption, such as the temperature, luminosity, seasonality, with the log of consumption data, it is possible for the proposed method by find patterns and correlations between the different sources of data and therefore improves the forecasting performance. A case study based on real data from a pilot microgrid located at the GECAD campus in the Polytechnic of Porto is presented. Data from the pilot buildings are used, and the results are compared to those achieved by several states of the art forecasting approaches. Results show that the proposed method can reach lower forecasting errors than the other considered methods.
- Energy consumption forecasting using neuro-fuzzy inference systems: Thales TRT building case studyPublication . Jozi, Aria; Pinto, Tiago; Praça, Isabel; Ramos, Sérgio; Vale, Zita; Goujon, Benedicte; Petrisor, TeodoraElectrical energy consumption forecasting is, nowadays, essential in order to deal with the new paradigm of consumers' active participation in the power and energy system. The uncertainty related to the variability of consumption is associated to numerous factors, such as consumers' habits, the environmental temperature, luminosity, etc. Current forecasting methods are not suitable to deal with such a combination of input variables, with often highly variable influence on the outcomes of the actual energy consumption. This paper presents a study on the application of five different methods based on fuzzy rule-based systems. This type of method is able to find associations between the distinct input variables, thus creating rules that support and improve the actual forecasting process. A case study is presented, showing the results of applying these five methods to predict the consumption of a real building: the Thales TRT building, in France.