Electricity consumption forecasting in office buildings: an artificial intelligence approach

The 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.This work has received funding from the European Union's Horizon 2020 research and innovation programme under project DOMINOES (grant agreement No 771066) and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2019.info:eu-repo/semantics/publishedVersio

Economic Operation of a Workplace EV Parking Lot under Different Operation Modes

In this paper, an electric vehicle (EV) charging station model at a workplace EV parking lot with energy storage system (ESS) and renewable energy sources (RESs) is proposed. Its economic operation under different operation modes is further explored. By comparing the paid charging mode at different prices with the free charging mode, the results show that although a sufficiently high charging price can obtain higher profit, the free charging model will bring greater profit growth with appropriate RES and ESS size as EVs will used for vehicle-to-grid (V2G) and grid-to-vehicle (G2V) transactions in return

Charge/Discharge Scheduling of Electric Vehicles and Battery Energy Storage in Smart Building: a Mix Binary Linear Programming model

Nowadays, the buildings have an important role on high demand of electricity energy. Therefore, the energy management of the buildings may have significant influence on reducing the electricity consumption. Moreover, Electric Vehicles (EVs) have been considering as a power storage devices in Smart Buildings (SBs) aiming to reduce the cost and consuming energy. Here, an energy management framework is proposed in which by considering the flexibility of the contracted power of each apartment, an optimal charging-discharging scheduled for EVs and Battery Energy Storage System (BESS) is defined over long time period to minimize the electricity cost of the building. The proposed model is design by a Mixed Binary Linear rogramming formulation (MBLP) that the charging and discharging of EVs as well as BESS in each period is treated as binary decision variables. In order to validate the model, a case study involving three scenarios are considered. The obtained results indicate a 15% reduction in total electricity consumption cost and consumption energy by the grid over a one year. Finally, the impact of capacity and charge/discharge rate of BESS on the power cost is analyzed and the optimal size of the BESS for assumed SB in the case study is also reported

Towards an evaluation of incentives and nudges for smart charging

Electric vehicles (EVs) are an important cornerstone to achieve transport decarbonization. Still, simultaneous charging of EVs when home charging increases peak demand, especially during evenings. Smart charging allows optimal distribution of load, thus preventing peak loads. Nevertheless, this incorporates certain risks for the EV user, e.g., unavailability of EVs for unplanned events. This might lead to a lack of user acceptance. This paper focuses on specific incentives and nudges, motivating users to adopt smart charging. We conducted an integrative literature review, bringing together literature from different areas. Possible incentives and nudges are monetary incentives, feedback, gamification, or smart charging as a default-setting. We conducted three focus groups with 13 EV users in Luxembourg to get first insights into which of those incentives and nudges they prefer. Preliminary results indicate that incentives and nudges should be individualized. In the future, we would use these first insights to develop a large-scale survey

A Stacked GRU-RNN-based Approach for Predicting Renewable Energy and Electricity Load for Smart Grid Operation

Predictions of renewable energy (RE) generation and electricity load are critical to smart grid operation. However, the prediction task remains challenging due to the intermittent and chaotic character of RE sources, and the diverse user behavior and power consumers. This paper presents a novel method for the prediction of RE generation and electricity load using improved stacked gated recurrent unit-recurrent neural network (GRU-RNN) for both uni-variate and multi-variate scenarios. First, multiple sensitive monitoring parameters or historical electricity consumption data are selected according to the correlation analysis to form the input data. Second, a stacked GRU-RNN using a simplified GRU is constructed with improved training algorithm based on AdaGrad and adjustable momentum. The modified GRU-RNN structure and improved training method enhance training efficiency and robustness. Third, the stacked GRU-RNN is used to establish an accurate mapping between the selected variables and RE generation or electricity load due to its self-feedback connections and improved training mechanism. The proposed method is verified by using two experiments: prediction of wind power generation using multiple weather parameters and prediction of electricity load with historical energy consumption data. The experimental results demonstrate that the proposed method outperforms state-of-the-art methods of machine learning or deep learning in achieving an accurate energy prediction for effective smart grid operation

Charge/Discharge Scheduling of Electric Vehicles and Battery Energy Storage in Smart Building: a Mix Binary Linear Programming model

Nowadays, the buildings have an important role on high demand of electricity energy. Therefore, the energy management of the buildings may have significant influence on reducing the electricity consumption. Moreover, Electric Vehicles (EVs) have been considering as a power storage devices in Smart Buildings (SBs) aiming to reduce the cost and consuming energy. Here, an energy management framework is proposed in which by considering the flexibility of the contracted power of each apartment, an optimal charging-discharging scheduled for EVs and Battery Energy Storage System (BESS) is defined over long time period to minimize the electricity cost of the building. The proposed model is design by a Mixed Binary Linear rogramming formulation (MBLP) that the charging and discharging of EVs as well as BESS in each period is treated as binary decision variables. In order to validate the model, a case study involving three scenarios are considered. The obtained results indicate a 15% reduction in total electricity consumption cost and consumption energy by the grid over a one year. Finally, the impact of capacity and charge/discharge rate of BESS on the power cost is analyzed and the optimal size of the BESS for assumed SB in the case study is also reported

Maximizing Smart Charging of EVs: The Impact of Privacy and Money on Data Sharing

Smart charging has the potential to shift peak load to times of lower demand, which better exploits renewable generation and enhances grid resilience. For increased effectiveness, smart charging requires access to data that consumers might be hesitant to share. To explore which data consumers would share and which factors influence this decision, we adopt the Barth and de Jong’s risk-benefit calculation framework to smart charging and conduct an online-survey (n = 479). We find that most respondents who would share charging details with a smart charging application, are ambivalent about location data and would never share calendar details. When presented with concrete monetary rewards, participants lose their initial reservations and would share all data for an amount dependent on the data’s sensitivity. Thus, our study contributes to research on the privacy paradox by highlighting the importance of calculations between perceived risks and benefits for the decision to share data

Optimal Scheduling of Home Energy Management System with Plug-in Electric Vehicles Using Model Predictive Control

abstract: With the growing penetration of plug-in electric vehicles (PEVs), the impact of the PEV charging brought to the utility grid draws more and more attention. This thesis focused on the optimization of a home energy management system (HEMS) with the presence of PEVs. For a household microgrid with photovoltaic (PV) panels and PEVs, a HEMS using model predictive control (MPC) is designed to achieve the optimal PEV charging. Soft electric loads and an energy storage system (ESS) are also considered in the optimization of PEV charging in the MPC framework. The MPC is solved through mixed-integer linear programming (MILP) by considering the relationship of energy flows in the optimization problem. Through the simulation results, the performance of optimization results under various electricity price plans is evaluated. The influences of PV capacities on the optimization results of electricity cost are also discussed. Furthermore, the hardware development of a microgrid prototype is also described in this thesis.Dissertation/ThesisMasters Thesis Engineering 201