AI based residential load forecasting

The increasing levels of energy consumption worldwide is raising issues with respect to surpassing supply limits, causing severe effects on the environment, and the exhaustion of energy resources. Buildings are one of the most relevant sectors in terms of energy consumption in the world. Many researches have been carried out in the recent years with primary concentration on efficient Home or Building Management Systems. In addition, by increasing renewable energy penetration, modern power grids demand more accurate consumption predictions to provide the optimized power supply which is stochastic in nature. This study will present an analytic comparison of day-ahead load forecasting during a period of two years by applying AI based data driven models. The unit of analysis in this thesis project is based on households smart meter data in England. The collected and collated data for this study includes historical electricity consumption of 75 houses over two years of 2012 to 2014 city of London. Predictive models divided in two main forecasting groups of deterministic and probabilistic forecasting. In deterministic step, Random Forest Regression and MLP Regression employed to make a forecasting models. In the probabilistic phase,DeepAR, FFNN and Gaussian Process Estimator were employed to predict days ahead load forecasting. The models are trained based on subset of various groups of customers with registered diversified load volatility level. Daily weather data are also added as new feature in this study into subset to check model sensitivity to external factors and validate the performance of the model. The results of implemented models are evaluated by well-known error metrics as RMSE,MAE, MSE and CRPS separately for each phase of this study. The findings of this master thesis study shows that the Deep Learning methods of FNN, DeepAR and MLP compared to other utilized methods like Random Forest and Gaussian provide better data prediction reslts in terms of less deviance to real load trend, lower forecasting error and computation time. Considering probabilistic forecasting methods it is observed that DeepAR can provide better results than FFNN and Gaussian Process model. Although the computation time of FFNN was lower than other

Deep-based conditional probability density function forecasting of residential loads

This paper proposes a direct model for conditional probability density forecasting of residential loads, based on a deep mixture network. Probabilistic residential load forecasting can provide comprehensive information about future uncertain-ties in demand. An end-to-end composite model comprising convolution neural networks (CNNs) and gated recurrent unit (GRU) is designed for probabilistic residential load forecasting. Then, the designed deep model is merged into a mixture density network (MDN) to directly predict probability density functions (PDFs). In addition, several techniques, including adversarial training, are presented to formulate a new loss function in the direct probabilistic residential load forecasting (PRLF) model. Several state-of-the-art deep and shallow forecasting models are also presented in order to compare the results. Furthermore, the effectiveness of the proposed deep mixture model in characterizing predicted PDFs is demonstrated through comparison with kernel density estimation, Monte Carlo dropout, a combined probabilistic load forecasting method and the proposed MDN without adversarial trainin

Adaptive individual residential load forecasting based on deep learning and dynamic mirror descent

With a growing penetration of renewable energy generation in the modern power networks, it has become highly challenging for network operators to balance electricity supply and demand. Residential load forecasting nowadays plays an increasingly important role in this aspect and facilitates various interactions between power networks and electricity users. While numerous research works have been proposed targeting at aggregate residential load forecasting, only a few efforts have been made towards individual residential load forecasting. The issue of volatility of individual residential load has never been addressed in forecasting. Thus, to fill this gap, this paper presents a deep learning method empowered with dynamic mirror descent for adaptive individual residential load forecasting. The proposed method is evaluated on a real-life Irish residential load dataset, and the experimental results show that it improves the prediction accuracy by 9.1% and 11.6% in the aspects of RMSE and MAE respectively in comparison with a benchmark method

Deep neural network for load forecasting centred on architecture evolution

Nowadays, electricity demand forecasting is critical for electric utility companies. Accurate residential load forecasting plays an essential role as an individual component for integrated areas such as neighborhood load consumption. Short-term load forecasting can help electric utility companies reduce waste because electric power is expensive to store. This paper proposes a novel method to evolve deep neural networks for time series forecasting applied to residential load forecasting. The approach centres its efforts on the neural network architecture during the evolution. Then, the model weights are adjusted using an evolutionary optimization technique to tune the model performance automatically. Experimental results on a large dataset containing hourly load consumption of a residence in London, Ontario shows that the performance of unadjusted weights architecture is comparable to other state-of-the-art approaches. Furthermore, when the architecture weights are adjusted the model accuracy surpassed the state-of-the-art method called LSTM one shot by 3.0%

Comparative analysis of residential load forecasting with different levels of aggregation

Microgrids need a robust residential load forecasting. As a consequence, this highlights the problem of predicting electricity consumption in small amounts of households. The individual demand curve is volatile, and more difficult to forecast than the aggregated demand curve. For this reason, Mean Absolute Percentage Error (MAPE) varies in a large range (of 1% to 45%), depending on the number of consumers analyzed. Different levels of aggregation of household consumers that can be used in microgrids are analyzed; the load forecasting of the single consumer and aggregated consumers are compared. The forecasting methodology used is the most consolidated of Recurrent Neural Networks, i.e., LSTM. The dataset used contains 920 residential consumers belonging to the Commission for Energy Regulation (CER), a control group that is in the Irish Social Science Data Archive (ISSDA) repository. The result shows that the forecasting of groups of more than 20 aggregated consumers has a lower MAPE that individual forecasting. On the other hand, individual forecasting is better for groups with fewer than 10 consumers

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%

Transfer Learning by Similarity Centred Architecture Evolution for Multiple Residential Load Forecasting

The development from traditional low voltage grids to smart systems has become extensive and adopted worldwide. Expanding the demand response program to cover the residential sector raises a wide range of challenges. Short term load forecasting for residential consumers in a neighbourhood could lead to a better understanding of low voltage consumption behaviour. Nevertheless, users with similar characteristics can present diversity in consumption patterns. Consequently, transfer learning methods have become a useful tool to tackle differences among residential time series. This paper proposes a method combining evolutionary algorithms for neural architecture search with transfer learning to perform short term load forecasting in a neighbourhood with multiple household load consumption. The approach centres its efforts on neural architecture search using evolutionary algorithms. The neural architecture evolution process retains the patterns of the centre-most house, and later the architecture weights are adjusted for each house in a multihouse set from a neighbourhood. In addition, a sensitivity analysis was conducted to ensure model performance. Experimental results on a large dataset containing hourly load consumption for ten houses in London, Ontario showed that the performance of the proposed approach performs better than the compared techniques. Moreover, the proposed method presents the average accuracy performance of 3.17 points higher than the state-of-the-art LSTM one shot method

Federated Learning for Short-term Residential Load Forecasting

Load forecasting is an essential task performed within the energy industry to help balance supply with demand and maintain a stable load on the electricity grid. As supply transitions towards less reliable renewable energy generation, smart meters will prove a vital component to facilitate these forecasting tasks. However, smart meter adoption is low among privacy-conscious consumers that fear intrusion upon their fine-grained consumption data. In this work we propose and explore a federated learning (FL) based approach for training forecasting models in a distributed, collaborative manner whilst retaining the privacy of the underlying data. We compare two approaches: FL, and a clustered variant, FL+HC against a non-private, centralised learning approach and a fully private, localised learning approach. Within these approaches, we measure model performance using RMSE and computational efficiency. In addition, we suggest the FL strategies are followed by a personalisation step and show that model performance can be improved by doing so. We show that FL+HC followed by personalisation can achieve a ~5% improvement in model performance with a ~10x reduction in computation compared to localised learning. Finally we provide advice on private aggregation of predictions for building a private end-to-end load forecasting application

Application of Deep Learning Long Short-Term Memory in Energy Demand Forecasting

The smart metering infrastructure has changed how electricity is measured in both residential and industrial application. The large amount of data collected by smart meter per day provides a huge potential for analytics to support the operation of a smart grid, an example of which is energy demand forecasting. Short term energy forecasting can be used by utilities to assess if any forecasted peak energy demand would have an adverse effect on the power system transmission and distribution infrastructure. It can also help in load scheduling and demand side management. Many techniques have been proposed to forecast time series including Support Vector Machine, Artificial Neural Network and Deep Learning. In this work we use Long Short Term Memory architecture to forecast 3-day ahead energy demand across each month in the year. The results show that 3-day ahead demand can be accurately forecasted with a Mean Absolute Percentage Error of 3.15%. In addition to that, the paper proposes way to quantify the time as a feature to be used in the training phase which is shown to affect the network performance