Disaggregation of net-metered advanced metering infrastructure data to estimate photovoltaic generation

2019 Fall.Includes bibliographical references.Advanced metering infrastructure (AMI) is a system of smart meters and data management systems that enables communication between a utility and a customer's premise, and can provide real time information about a solar array's production. Due to residential solar systems typically being configured behind-the-meter, utilities often have very little information about their energy generation. In these instances, net-metered AMI data does not provide clear insight into PV system performance. This work presents a methodology for modeling individual array and system-wide PV generation using only weather data, premise AMI data, and the approximate date of PV installation. Nearly 850 homes with installed solar in Fort Collins, Colorado, USA were modeled for up to 36 months. By matching comparable periods of time to factor out sources of variability in a building's electrical load, algorithms are used to estimate the building's consumption, allowing the previously invisible solar generation to be calculated. These modeled outputs are then compared to previously developed white-box physical models. Using this new AMI method, individual premises can be modeled to agreement with physical models within ±20%. When modeling portfolio-wide aggregation, the AMI method operates most effectively in summer months when solar generation is highest. Over 75% of all days within three years modeled are estimated to within ±20% with established methods. Advantages of the AMI model with regard to snow coverage, shading, and difficult to model factors are discussed, and next-day PV prediction using forecasted weather data is also explored. This work provides a foundation for disaggregating solar generation from AMI data, without knowing specific physical parameters of the array or using known generation for computational training

An approach for the estimation of the aggregated photovoltaic power generated in several European countries from meteorological data

Classical approaches to the calculation of the photovoltaic (PV) power generated in a region from meteorological data require the knowledge of the detailed characteristics of the plants, which are most often not publicly available. An approach is proposed with the objective to obtain the best possible assessment of power generated in any region without having to collect detailed information on PV plants. The proposed approach is based on a model of PV plant coupled with a statistical distribution of the prominent characteristics of the configuration of the plant and is tested over Europe. The generated PV power is first calculated for each of the plant configurations frequently found in a given region and then aggregated taking into account the probability of occurrence of each configuration. A statistical distribution has been constructed from detailed information obtained for several thousands of PV plants representing approximately 2 % of the total number of PV plants in Germany and was then adapted to other European countries by taking into account changes in the optimal PV tilt angle as a function of the latitude and meteorological conditions. The model has been run with bias-adjusted ERA-interim data as meteorological inputs. The results have been compared to estimates of the total PV power generated in two countries: France and Germany, as provided by the corresponding transmission system operators. Relative RMSE of 4.2 and 3.8 % and relative biases of −2.4 and 0.1 % were found with three-hourly data for France and Germany. A validation against estimates of the country-wide PV-power generation provided by the ENTSO-E for 16 European countries has also been conducted. This evaluation is made difficult by the uncertainty on the installed capacity corresponding to the ENTSO-E data but it nevertheless allows demonstrating that the model output and TSO data are highly correlated in most countries. Given the simplicity of the proposed approach these results are very encouraging. The approach is particularly suited to climatic timescales, both historical and future climates, as demonstrated here

Progress in regional PV power forecasting: A sensitivity analysis on the Italian case study

The increasing penetration of PV generation, driven by climate strategies and objectives, calls for accurate production forecasting to mitigate the negative effects associated with inherent variability, such as overgeneration, grid instability, supplementary reserve request. The regional PV power forecasting is crucial for Transmission and Distribution system operators for a better management of energy flows. In this work many aspects of regional PV power forecasting are investigated, by means of a comparison of six different forecasting models applied to predict the hourly production of the following days on six Italian bidding zones for one year. In particular, the work shows that the forecasting accuracy is mainly affected by the algorithm and its pre and post processing, with a range of 30% in performance accuracy, while it is less impacted by the forecasting horizon. It has been verified that the accuracy in the irra- diation prediction, used in input to the power forecasting algorithm, has less impact compared to single plants. The work confirms the performance improvement which can be obtained by increasing the size of the area to which the prediction refers, through a comparison between the forecasting at bidding zone and national level. Finally, we show that the larger the controlled forecast area, the smaller the impact on the forecast accuracy due to the non-uniform spatial and capacity distribution of the PV fleet. This means that as the size of the region increases, the average irradiance progressively becomes the best PV power predictor. We refer to this phenomenon as: “input smoothing effect"

Non-intrusive load monitoring under residential solar power influx

This paper proposes a novel Non-Intrusive Load Monitoring (NILM) method for a consumer premises with a residentially installed solar plant. This method simultaneously identifies the amount of solar power influx as well as the turned ON appliances, their operating modes, and power consumption levels. Further, it works effectively with a single active power measurement taken at the total power entry point with a sampling rate of 1 Hz. First, a unique set of appliance and solar signatures were constructed using a high-resolution implementation of Karhunen Loéve expansion (KLE). Then, different operating modes of multi-state appliances were automatically classified utilizing a spectral clustering based method. Finally, using the total power demand profile, through a subspace component power level matching algorithm, the turned ON appliances along with their operating modes and power levels as well as the solar influx amount were found at each time point. The proposed NILM method was first successfully validated on six synthetically generated houses (with solar units) using real household data taken from the Reference Energy Disaggregation Dataset (REDD) - USA. Then, in order to demonstrate the scalability of the proposed NILM method, it was employed on a set of 400 individual households. From that, reliable estimations were obtained for the total residential solar generation and for the total load that can be shed to provide reserve services. Finally, through a developed prediction technique, NILM results observed from 400 households during four days in the recent past were utilized to predict the next day’s total load that can be shed

Data-Driven Approach to Forecast Heat Consumption of Buildings with High-Priority Weather Data

By increasing the penetration of renewable energies in district heating (DH), the intermittency of the supply-side increases for heating service providers. Therefore, forecasting the energy consumption of buildings is needed in order to hedge against renewable power intermittency. This paper investigates the application of data-driven approaches to forecast the heat consumption of buildings in the winter, using high-priority weather data. The residential buildings are connected to mixing loops of DH to supply space heating and hot water. The heating consumption of the building is calculated using sensor data, including inflow/outflow temperature and mass flow. Principal component analysis (PCA) is applied to determine the key weather data affecting heat energy consumption. Then, the study compares the competences of artificial neural networks (ANNs), linear regression models (LRM), and k-nearest neighbors (k-NN) in forecasting heat consumption, using informative data. Based on the PCA analysis, ambient temperature and solar irradiation are shown to be the highest priority weather data, contributing to 40.6% and 29.2% of heat energy forecasting, respectively. Furthermore, the ANN exhibits a forecasting accuracy of more than 50% higher than LRM and k-NN