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Publications

Rating and Remunerating the Load Shifting by Consumers Participating in Demand Response Programs

Publication . Silva, Cátia; Faria, Pedro; Vale, Zita

Effective and active consumers providing flexibility through Demand Response (DR) programs have three important aspects: rating each consumer according to previous participation, remuneration of that participation, and determining the rebound effect of consumption after the event. In this paper, the authors design a rate to classify and select the proper participants for a DR event considering the context in which the event is triggered. The aggregator estimated the shifting of consumption to periods after the event is modeled, and the respective remuneration is estimated under different scenarios. This shifting can be done in several time frames in the future. The scenarios are developed to test the acceptable time range in which the load should be allocated according to the rebound effect. The results show that a higher time range can avoid huge peak consumption, optimizing the system operation with benefits for consumers, DSO, and the aggregator.

2022Journal article

Restricted access

Short Time Electricity Consumption Forecast in an Industry Facility

Publication . Ramos, Daniel; Faria, Pedro; Vale, Zita; Correia, Regina

The work in this article uses artificial neural networks and support vector machine to forecast electricity consumption in an industrial facility. The main objective is to show that such a problem should be treated with a contextual approach that identifies the most adequate technic in each moment for a single building, contrary to the previous works in the literature that compare the accuracy of each method for the complete data set representing aggregated loads. 72 different algorithms have been implemented and tested. After that, the three most suitable ones are selected in order to support the automated decisions of the best algorithm according to the context. In this way, the implemented methodology finds the best method for the prediction of each 5 min. It can be later used to update the production planning in the industrial facility. It also discussed the size of historical data and the most suitable learning parameters for each method. The case study includes test data for one week and more than one year of training data.

2022Journal article

Restricted access

A Contextual Reinforcement Learning Approach for Electricity Consumption Forecasting in Buildings

Publication . Ramos, Daniel; Faria, Pedro; Gomes, Luis; Vale, Zita

The energy management of buildings plays a vital role in the energy sector. With that in mind, and targeting an accurate forecast of electricity consumption, in the present paper is aimed to provide decision on the best prediction algorithm for each context. It may also increase energy usage related with renewables. In this way, the identification of different contexts is an advantage that may improve prediction accuracy. This paper proposes an innovative approach where a decision tree is used to identify different contexts in energy patterns. One week of five-minutes data sampling is used to test the proposed methodology. Each context is evaluated with a decision criterion based on reinforcement learning to find the best suitable forecasting algorithm. Two forecasting models are approached in this paper, based on K-Nearest Neighbor and Artificial Neural Networks, to illustrate the application of the proposed methodology. The reinforcement learning criterion consists of using the Multiarmed Bandit algorithm. The obtained results validate the adequacy of the proposed methodology in two case-studies: building; and industry.

2022Journal article

Open access

Selection of features in reinforcement learning applied to energy consumption forecast in buildings according to different contexts

Publication . Ramos, Daniel; Faria, Pedro; Gomes, Luis; Campos, P.; Vale, Zita

The management of buildings responsible for the energy storage and control can be optimized with the support of forecasting techniques. These are essential on the finding of load consumption patterns being these last involved in decisions that analyze which forecasting technique results in more accurate predictions in each context. This paper considers two forecasting methods known as artificial neural network and k-nearest neighbor involved in the prediction of consumption of a building composed by devices recording consumption and sensors data. The forecasts are performed in five minutes periods with the forecasting technique taken into account as a potential to improve the accuracy of predictions. The decision making considers the Multi-armed Bandit in reinforcement learning context to find the best suitable algorithm in each five minutes period thus improving the predictions accuracy in forecasting. The reinforcement learning has been tested in upper confidence bound and greedy algorithms with several exploration alternatives. In the case-study, three contexts have been analyzed.

2022Journal article

Open access

Electric Mobility: An Overview of the Main Aspects Related to the Smart Grid

Publication . Barreto, Rúben; Faria, Pedro; Vale, Zita

Electric mobility has become increasingly prominent, not only because of the potential to reduce greenhouse gas emissions but also because of the proven implementations in the electric and transport sector. This paper, considering the smart grid perspective, focuses on the financial and economic benefits related to Electric Vehicle (EV) management in Vehicle-to-Building (V2B), Vehicle-to-Home (V2H), and Vehicle-to-Grid (V2G) technologies. Vehicle-to-Everything is also approached. The owners of EVs, through these technologies, can obtain revenue from their participation in the various ancillary and other services. Similarly, providing these services makes it possible to increase the electric grid’s service quality, reliability, and sustainability. This paper also highlights the different technologies mentioned above, giving an explanation and some examples of their application. Likewise, it is presented the most common ancillary services verified today, such as frequency and voltage regulation, valley filling, peak shaving, and renewable energy supporting and balancing. Furthermore, it is highlighted the different opportunities that EVs can bring to energy management in smart grids. Finally, the SWOT analysis is highlighted for V2G technology.

2022Journal article

Open access