Single residential load forecasting using deep learning and image encoding techniques

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. The integration of more renewable energy resources into distribution networks makes the operation of these systems more challenging compared to the traditional passive networks. This is mainly due to the intermittent behavior of most renewable resources such as solar and wind generation. There are many different solutions being developed to make systems flexible such as energy storage or demand response. In the context of demand response, a key factor is to estimate the amount of load over time properly to better manage the demand side. There are many different forecasting methods, but the most accurate solutions are mainly found for the prediction of aggregated loads at the substation or building levels. However, more effective demand response from the residential side requires prediction of energy consumption at every single household level. The accuracy of forecasting loads at this level is often lower with the existing methods as the volatility of single residential loads is very high. In this paper, we present a hybrid method based on time series image encoding techniques and a convolutional neural network. The results of the forecasting of a real residential customer using different encoding techniques are compared with some other existing forecasting methods including SVM, ANN, and CNN. Without CNN, the lowest mean absolute percentage of error (MAPE) for a 15 min forecast is above 20%, while with existing CNN, directly applied to time series, an MAPE of around 18% could be achieved. We find the best image encoding technique for time series, which could result in higher accuracy of forecasting using CNN, an MAPE of around 12%

Deep learning based forecasting of individual residential loads using recurrence plots

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. High penetration of renewable energy resources in distribution systems brings more uncertainty for system control and management due their intermittent behaviour. In this context, besides generation side, demand side should be also controlled and managed. Since demand side has variant flexibility over time, in order to timely facilitate Demand Response (DR), distribution system operators (DSO) should be aware of DR potential in advance to see whether it is sufficient for different services, and how much and when to send DR signals. This indeed requires accurate short-term or medium-term load forecasting. There are many methods for predicting aggregated loads, but more effective DR schemes should involve individual residential households which would require load forecasting of single residential loads. This is much more challenging due to high volatility in load curves of single customers. In this paper, we present a novel method of forecasting individual household power consumption using recurrence plots and deep learning. We use Convolutional Neural Network (CNN) for such a two-dimensional deep learning approach, and compare it with one-dimensional CNN, as well as Support Vector Machine (SVM) and Artificial Neural Network (ANN). Demonstrating some experimental tests on a real case proved that our approach outperforms the other existing solutions

Nonintrusive Load Monitoring (NILM) Using a Deep Learning Model with a Transformer-Based Attention Mechanism and Temporal Pooling

Nonintrusive load monitoring (NILM) is an important technique for energy management and conservation. In this paper, a deep learning model based on an attention mechanism, temporal pooling, residual connections, and transformers is proposed. This article presents a novel approach for NILM to accurately discern energy consumption patterns of individual household appliances. The proposed method entails a sequence of layers, including encoders, transformers, attention, temporal pooling, and residual connections, offering a comprehensive solution for NILM while effectively capturing appliance-specific energy usage in a household. The proposed model was evaluated using UK-DALE, REDD, and REFIT datasets in both seen and unseen cases. It shows that the proposed model in this paper performs better than other methods stated in other papers in terms of F1-score and total error of the results (in terms of SAE). This model achieved an F1-score equal to 92.96 as well as a total SAE equal to −0.036, which shows its effectiveness in accurately diagnosing and estimating the energy consumption of individual home appliances. The findings of this research show that the proposed model can be a tool for energy management in residential and commercial buildings

<strong>Antibacterial effects of Arctium lappa and Artemesia absinthium extracts in laboratory conditions</strong>

Introduction: Arctium lappa (Great burduck) and Artemesia absinthium are medicinal plants that some of their antibacterial and antivirus properties have been suggested in nutritional industries. The objective of this research was to study the effects of A. lappa and A. absinthium on some microorganisms including Pseudomonads aeraginosa, Haemophilus influenza, Bacillus subtilis, Bacillus cereus, Klebsiella pneumonia and Staphylococcus aureus. Methods: Extracts were prepared by maceration method and tested on Mueller Hinton agar medium based on disc diffusion method. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were determined by micro-dilution method. Antibiotic disks used for controlling and standardizing the examination. Results: The extracts of A. lappa and A. absinthium had significant effect on S. aureus. The MIC and MBC concentrations of the extract of A. lappa on B. subtilis were respectively 600 and 750 mg/ml. Also, these values were 230 and 540 mg/ml for H. influenza. Extract of A. absinthium showed more inhibitory effect on B. subtilis. All extracts showed inhibitory effect on B. cereus. The extracts of A. lappa and A. absinthium had inhibitory effects on H. influenza and P. aeraginosa. Among antibiotics, only Ofloxacin and Ciprofloxacin had effects on H. influenza. Extract of A. lappa showed flimsy effect on K. pneumonia, while extract of A. absinthium had no effect on this bacterium. Conclusion: Due to the effects of A. lappa and A. absinthium on some bacteria, they might be good substitutes for synthetic substances

Time-of-Use Tariff with Local Wind Generation

Renewable energy, such as wind power, is known to significantly reduce system costs and carbon emissions. However, traditional Time of Use (ToU) tariffs fail to account for local energy generation. To overcome this limitation, we propose a mechanism for calculating new ToU tariffs that incorporates Agile ToU and local energy resources, such as a wind farm. By partially supplying local consumption, wind energy can reduce electricity costs for consumers and encourage load shifting towards peak renewable energy production periods. We demonstrate the effectiveness of the proposed mechanism by testing it on a case study of a residential area in Wales, UK, where electricity would be partially supplied by a nearby wind farm with 5 turbines through a Power Purchase Agreement (PPA). The results show that the new tariff significantly reduces electricity bills

Sequence-to-Sequence Model with Transformer-based Attention Mechanism and Temporal Pooling for Non-Intrusive Load Monitoring

This paper presents a novel Sequence-to-Sequence (Seq2Seq) model based on a transformer-based attention mechanism and temporal pooling for Non-Intrusive Load Monitoring (NILM) of smart buildings. The paper aims to improve the accuracy of NILM by using a deep learning-based method. The proposed method uses a Seq2Seq model with a transformer-based attention mechanism to capture the long-term dependencies of NILM data. Additionally, temporal pooling is used to improve the model's accuracy by capturing both the steady-state and transient behavior of appliances. The paper evaluates the proposed method on a publicly available dataset and compares the results with other state-of-the-art NILM techniques. The results demonstrate that the proposed method outperforms the existing methods in terms of both accuracy and computational efficiency

Non-Intrusive Load Monitoring (NILM) using Deep Neural Networks: A Review

Demand-side management now encompasses more residential loads. To efficiently apply demand response strategies, it's essential to periodically observe the contribution of various domestic appliances to total energy consumption. Non-intrusive load monitoring (NILM), also known as load disaggregation, is a method for decomposing the total energy consumption profile into individual appliance load profiles within the household. It has multiple applications in demand-side management, energy consumption monitoring, and analysis. Various methods, including machine learning and deep learning, have been used to implement and improve NILM algorithms. This paper reviews some recent NILM methods based on deep learning and introduces the most accurate methods for residential loads. It summarizes public databases for NILM evaluation and compares methods using standard performance metrics

Short-Term Load Forecasting Using Time Pooling Deep Recurrent Neural Network

Integration of renewable energy sources and emerging loads like electric vehicles to smart grids brings more uncertainty to the distribution system management. Demand Side Management (DSM) is one of the approaches to reduce the uncertainty. Some applications like Nonintrusive Load Monitoring (NILM) can support DSM, however they require accurate forecasting on high resolution data. This is challenging when it comes to single loads like one residential household due to its high volatility. In this paper, we review some of the existing Deep Learning-based methods and present our solution using Time Pooling Deep Recurrent Neural Network. The proposed method augments data using time pooling strategy and can overcome overfitting problems and model uncertainties of data more efficiently. Simulation and implementation results show that our method outperforms the existing algorithms in terms of RMSE and MAE metrics

Markov Chain Modelling-Based Approach to Reserve Electric Vehicles in Parking Lots for Distribution System Energy Management

Integration of renewable energy resources in distribution networks with intermittent behaviour increases the challenge of power balance in transmission systems. To mitigate the undesired impacts, transmission operator involves distribution operators to get local contribution from their flexible resources. In this paper, we address the flexibility offered by some electric car sharing agents which can serve some reserve capacity to distribution system. A Markov Chain modelling based approach is proposed to support system operator to properly estimate the number of electric vehicles required to be booked in advance as reserve. Underestimation would result in imperfect demand correction, and overestimation would imply extra costs. Using a realistic case under a near future scenario of high PV integration and EV accommodation, we demonstrate the contribution of our approach to this problem of planning or scheduling. Obtained results quantifies the performance of the proposed method in terms of average energy difference based on number of EVs. The results can be used as a basis to decide the appropriate number of EV reservations