Traditional load analysis is facing challenges with the new electricity usage patterns due to demand response as well as increasing deployment of distributed generations, including photovoltaics (PV), electric vehicles (EV), and energy storage systems (ESS). At the transmission system, despite of irregular load behaviors at different areas, highly aggregated load shapes still share similar characteristics. Load clustering is to discover such intrinsic patterns and provide useful information to other load applications, such as load forecasting and load modeling. This paper proposes an efficient submodular load clustering method for transmission-level load areas. Robust principal component analysis (R-PCA) firstly decomposes the annual load profiles into low-rank components and sparse components to extract key features. A novel submodular cluster center selection technique is then applied to determine the optimal cluster centers through constructed similarity graph. Following the selection results, load areas are efficiently assigned to different clusters for further load analysis and applications. Numerical results obtained from PJM load demonstrate the effectiveness of the proposed approach.
Natural gas is the most important energy source in Italy: it fuels thermoelectric power plants, industrial facilities and domestic heating. Gas demand forecasting is a critical task for any energy provider as it impacts on pipe reservation and stock planning. In this paper, the one-day-ahead forecasting of Italian daily residential gas demand is studied. Five predictors are developed and compared: Ridge Regression, Gaussian Process, k-Nearest Neighbour, Artificial Neural Network, and Torus Model. Preprocessing and feature selection are also discussed in detail. Concerning the prediction error, a theoretical bound on the best achievable root mean square error is worked out assuming ideal conditions, except for the inaccuracy of meteorological temperature forecasts, whose effects are properly propagated. The best predictors, namely the Artificial Neural Network and the Gaussian Process, achieve an RMSE which is twice the performance limit, suggesting that precise predictions of residential gas demand can be achieved at country level.
The owner of the wrecked Fukushima No. 1 power plant is trying this week to touch melted fuel at the bottom of the plant for the first time since the disaster almost eight years ago, a tiny but key step toward retrieving the radioactive material amid a ¥21.5 trillion ($195 billion) cleanup effort. Tokyo Electric Power Co. Holdings Inc. will on Wednesday insert a robot developed by Toshiba Corp. to make contact with material believed to contain melted fuel inside the containment vessel of the unit 2 reactor, one of three units that melted down after the March 2011 earthquake and tsunami. "We plan to confirm if we can move or lift the debris or if it crumbles," Joji Hara, a spokesman for Tepco said by phone Friday. Tepco doesn't plan to collect samples during the survey. The country is seeking to clean up the Fukushima disaster, the world's worst atomic accident since Chernobyl, which prompted a mass shutdown of its reactors.
In her article in The MIT Technology Review--"Giving Algorithms a Sense of Uncertainty Could Make Them More Ethical" (January 18, 2019)--Karen Hao reached out to Cornell CS Professor Carla Gomes to ask if Peter Eckersley (and his Partnership on AI) is onto something in his approach to considering partial orders of solutions with respect to multiple, often conflicting, objectives, and possibly introducing uncertainty into AI systems, especially those addressing decision making and moral dilemmas. Eckersley says: "We as humans want multiple incompatible things. There are many high-stakes situations where it's actually inappropriate--perhaps dangerous--to program in a single objective function that tries to describe your ethics." Supportively, Gomes remarks: "The overall problem is very complex. It will take a body of research to address all issues, but Peter's approach is making an important step in the right direction."
In enterprise AI, C3 (formerly C3 IoT) is amassing an impressive and seemingly unmatched record, one that the company has extended with its latest win, the expansion of a five-year engagement with Enel, Europe's largest power utility, to encompass nearly 50 million smart meters in homes and businesses. This follows C3 contract wins last year with Royal Dutch Shell, the U.S. Air Force and 3M, along with partnerships with AWS, Google Cloud and Microsoft Azure. In the large utilities space, other customers include Con Edison, covering the New York metropolitan area, and Engie, one of the biggest utilities in France. The new contract (dollar amount not disclosed) expands on C3's existing, five-year engagement for Enel in Italy involving 32 million smart meters. C3 will provide the €74.6 billion utility with AI and smart grid analytics applications enabling Enel to deploy the Unified Virtual Data Lake, integrating data across its retail, distribution, trading, renewables and conventional generation businesses.
Due to the insufficient measurements in the distribution system state estimation (DSSE), full observability and redundant measurements are difficult to achieve without using the pseudo measurements. The matrix completion state estimation (MCSE) combines the matrix completion and power system model to estimate voltage by exploring the low-rank characteristics of the matrix. This paper proposes a robust matrix completion state estimation (RMCSE) to estimate the voltage in a distribution system under a low-observability condition. Tradition state estimation weighted least squares (WLS) method requires full observability to calculate the states and needs redundant measurements to proceed a bad data detection. The proposed method improves the robustness of the MCSE to bad data by minimizing the rank of the matrix and measurements residual with different weights. It can estimate the system state in a low-observability system and has robust estimates without the bad data detection process in the face of multiple bad data. The method is numerically evaluated on the IEEE 33-node radial distribution system. The estimation performance and robustness of RMCSE are compared with the WLS with the largest normalized residual bad data identification (WLS-LNR), and the MCSE.
Sense is a bright-orange box that sits in your electrical breaker box and gives in-depth insight into your home's entire power usage. The whole system is quite clever and--thankfully--free of any monthly charges. But it learns very slowly, and that's likely to frustrate you. Sense works by electromagnetically listening to the power flowing along the two hot wires that run from your electric meter to your breakers. By measuring the current flow a million times each second, Sense can observe changes in load with precise detail and, based on a machine-learning database, attempt to identify the footprint of different devices from the noise they generate.
Security-Constrained Unit Commitment (SCUC) is a fundamental problem in power systems and electricity markets. In practical settings, SCUC is repeatedly solved via Mixed-Integer Linear Programming, sometimes multiple times per day, with only minor changes in input data. In this work, we propose a number of machine learning (ML) techniques to effectively extract information from previously solved instances in order to significantly improve the computational performance of MIP solvers when solving similar instances in the future. Based on statistical data, we predict redundant constraints in the formulation, good initial feasible solutions and affine subspaces where the optimal solution is likely to lie, leading to significant reduction in problem size. Computational results on a diverse set of realistic and large-scale instances show that, using the proposed techniques, SCUC can be solved on average 12 times faster than conventional methods, with no negative impact on solution quality.
Energy disaggregation in a non-intrusive way estimates appliance level electricity consumption from a single meter that measures the whole house electricity demand. Recently, with the ongoing increment of energy data, there are many data-driven deep learning architectures being applied to solve the non-intrusive energy disaggregation problem. However, most proposed methods try to estimate the on-off state or the power consumption of appliance, which need not only large amount of parameters, but also hyper-parameter optimization prior to training and even preprocessing of energy data for a specified appliance. In this paper, instead of estimating on-off state or power consumption, we adapt a neural network to estimate the operational state change of appliance. Our proposed solution is more feasible across various appliances and lower complexity comparing to previous methods. The simulated experiments in the low sample rate dataset REDD show the competitive performance of the designed method, with respect to other two benchmark methods, Hidden Markov Model-based and Graph Signal processing-based approaches.
Forecasting natural gas demand is a key problem for energy providers, as it allows for efficient pipe reservation and power plant allocation, and enables effective price forecasting. We propose a study of Italian gas demand, with particular focus on industrial and thermoelectric components. To the best of our knowledge, this is the first work about these topics. After a preliminary discussion on the characteristics of gas demand, we apply several statistical learning models to perform day-ahead forecasting, including regularized linear models, random forest, support vector regression and neural networks. Moreover, we introduce four simple ensemble models and we compare their performance with the one of basic forecasters. The out-of-sample Mean Absolute Error (MAE) achieved on 2017 by our best ensemble model is 5.16 Millions of Standard Cubic Meters (MSCM), lower than 9.57 MSCM obtained by the predictions issued by SNAM, the Italian Transmission System Operator (TSO).