• Energy Research

To improve the design of the electricity infrastructure and the efficient deployment of distributed and renewable energy sources, a new paradigm for the energy supply chain is emerging, leading to the development of smart grids. There is a need to add intelligence at all levels in the grid, acting over various time horizons. Predicting the behavior of the energy system is crucial to mitigate potential uncertainties. An accurate energy prediction at the customer level will reflect directly in efficiency improvements in the whole system. However, prediction of building energy consumption is complex due to many influencing factors, such as climate, performance of thermal systems, and occupancy patterns. Therefore, current state-of-the-art methods are not able to confine the uncertainty at the building level due to the many fluctuations in influencing variables. As an evolution of artificial neural network (ANN)-based prediction methods, deep learning techniques are expected to increase the prediction accuracy by allowing higher levels of abstraction. In this paper, we investigate two newly developed stochastic models for time series prediction of energy consumption, namely Conditional Restricted Boltzmann Machine (CRBM) and Factored Conditional Restricted Boltzmann Machine (FCRBM). The assessment is made on a benchmark dataset consisting of almost four years of one minute resolution electric power consumption data collected from an individual residential customer. The results show that for the energy prediction problem solved here, FCRBM outperforms ANN, Support Vector Machine (SVM), Recurrent Neural Networks (RNN) and CRBM.

To improve the design of the electricity infrastructure and the efficient deployment of distributed and renewable energy sources, a new paradigm for the energy supply chain is emerging, leading to the development of smart grids. There is a need to add intelligence at all levels in the grid, acting over various time horizons. Predicting the behavior of the energy system is crucial to mitigate potential uncertainties. An accurate energy prediction at the customer level will reflect directly in efficiency improvements in the whole system. However, prediction of building energy consumption is complex due to many influencing factors, such as climate, performance of thermal systems, and occupancy patterns. Therefore, current state-of-the-art methods are not able to confine the uncertainty at the building level due to the many fluctuations in influencing variables. As an evolution of artificial neural network (ANN)-based prediction methods, deep learning techniques are expected to increase the prediction accuracy by allowing higher levels of abstraction. In this paper, we investigate two newly developed stochastic models for time series prediction of energy consumption, namely Conditional Restricted Boltzmann Machine (CRBM) and Factored Conditional Restricted Boltzmann Machine (FCRBM). The assessment is made on a benchmark dataset consisting of almost four years of one minute resolution electric power consumption data collected from an individual residential customer. The results show that for the energy prediction problem solved here, FCRBM outperforms ANN, Support Vector Machine (SVM), Recurrent Neural Networks (RNN) and CRBM.

To realize the goals of energy transition, becoming energy-neutral at the neighborhood level by sharing energy among clusters of heterogeneous buildings with local distributed energy resources (DERs), will play a vital role. However, uncertainties related to demand and renewable sources pose a major operational challenge to schedule the DERs. In this paper, a scenario-based mixed-integer linear programming (MILP) model is proposed for an energy management system (EMS) of a local energy community. The proposed EMS executes a stochastic day-ahead scheduling operation of multi-energy systems (MES). A set of scenarios are generated with the Gaussian mixture model (GMM) to consider uncertainties of demand and renewable sources. Moreover, Monte Carlo simulations (MCS) are performed to assess the effectiveness of the proposed EMS compared to the deterministic one. The proposed method is validated by using a real-world case study of a generic Dutch university medical campus in Amsterdam, the Netherlands. Two types of analysis are performed: one-day analysis and seasonal analysis. In both cases, in an average, the stochastic process outperforms the deterministic process considerably, in terms of cost, CO2 emission, imported electricity from grid and usage of local energy resources.

To realize the goals of energy transition, becoming energy-neutral at the neighborhood level by sharing energy among clusters of heterogeneous buildings with local distributed energy resources (DERs), will play a vital role. However, uncertainties related to demand and renewable sources pose a major operational challenge to schedule the DERs. In this paper, a scenario-based mixed-integer linear programming (MILP) model is proposed for an energy management system (EMS) of a local energy community. The proposed EMS executes a stochastic day-ahead scheduling operation of multi-energy systems (MES). A set of scenarios are generated with the Gaussian mixture model (GMM) to consider uncertainties of demand and renewable sources. Moreover, Monte Carlo simulations (MCS) are performed to assess the effectiveness of the proposed EMS compared to the deterministic one. The proposed method is validated by using a real-world case study of a generic Dutch university medical campus in Amsterdam, the Netherlands. Two types of analysis are performed: one-day analysis and seasonal analysis. In both cases, in an average, the stochastic process outperforms the deterministic process considerably, in terms of cost, CO2 emission, imported electricity from grid and usage of local energy resources.

Energy is a limited resource which has to be managed wisely, taking into account both supply-demand matching and capacity constraints in the distribution grid. One aspect of the smart energy management at the building level is given by the problem of real-time detection of flexible demand available. In this paper we propose the use of energy disaggregation techniques to perform this task. Firstly, we investigate the use of existing classification methods to perform energy disaggregation. A comparison is performed between four classifiers, namely Naive Bayes, k-Nearest Neighbors, Support Vector Machine and AdaBoost. Secondly, we propose the use of Restricted Boltzmann Machine to automatically perform feature extraction. The extracted features are then used as inputs to the four classifiers and consequently shown to improve their accuracy. The efficiency of our approach is demonstrated on a real database consisting of detailed appliance-level measurements with high temporal resolution, which has been used for energy disaggregation in previous studies, namely the REDD. The results show robustness and good generalization capabilities to newly presented buildings with at least 96% accuracy. To appear in IEEE PES General Meeting, 2016, Boston, USA

Energy is a limited resource which has to be managed wisely, taking into account both supply-demand matching and capacity constraints in the distribution grid. One aspect of the smart energy management at the building level is given by the problem of real-time detection of flexible demand available. In this paper we propose the use of energy disaggregation techniques to perform this task. Firstly, we investigate the use of existing classification methods to perform energy disaggregation. A comparison is performed between four classifiers, namely Naive Bayes, k-Nearest Neighbors, Support Vector Machine and AdaBoost. Secondly, we propose the use of Restricted Boltzmann Machine to automatically perform feature extraction. The extracted features are then used as inputs to the four classifiers and consequently shown to improve their accuracy. The efficiency of our approach is demonstrated on a real database consisting of detailed appliance-level measurements with high temporal resolution, which has been used for energy disaggregation in previous studies, namely the REDD. The results show robustness and good generalization capabilities to newly presented buildings with at least 96% accuracy. To appear in IEEE PES General Meeting, 2016, Boston, USA

The increasing penetration of intermittent Renewable Energy Sources (RES) such as PV generators in the residential distribution grid raises technical concerns. Residential energy storage is an enabling technology for mitigating the adverse effects of the increasing RES penetration and provides the necessary flexibility for its owner to participate in electricity markets or in market-based control schemes. This paper presents a model for calculating the "cost of use" for Li-Ion batteries. The cost of use depends on the usage pattern of the battery, indicated by the Depth of Discharge (DoD). Knowledge of the "cost of use" is needed for the economic optimization of the PV-storage system and the bid formulation in the electricity market.