Local Short Term Electricity Load Forecasting: Automatic Approaches

Short-Term Load Forecasting (STLF) is a fundamental component in the efficient management of power systems, which has been studied intensively over the past 50 years. The emerging development of smart grid technologies is posing new challenges as well as opportunities to STLF. Load data, collected at higher geographical granularity and frequency through thousands of smart meters, allows us to build a more accurate local load forecasting model, which is essential for local optimization of power load through demand side management. With this paper, we show how several existing approaches for STLF are not applicable on local load forecasting, either because of long training time, unstable optimization process, or sensitivity to hyper-parameters. Accordingly, we select five models suitable for local STFL, which can be trained on different time-series with limited intervention from the user. The experiment, which consists of 40 time-series collected at different locations and aggregation levels, revealed that yearly pattern and temperature information are only useful for high aggregation level STLF. On local STLF task, the modified version of double seasonal Holt-Winter proposed in this paper performs relatively well with only 3 months of training data, compared to more complex methods

Quantifying Forecast Uncertainty in the Energy Domain

This dissertation focuses on quantifying forecast uncertainties in the energy domain, especially for the electricity and natural gas industry. Accurate forecasts help the energy industry minimize their production costs. However, inaccurate weather forecasts, unusual human behavior, sudden changes in economic conditions, unpredictable availability of renewable sources (wind and solar), etc., represent uncertainties in the energy demand-supply chain. In the current smart grid era, total electricity demand from non-renewable sources influences by the uncertainty of the renewable sources. Thus, quantifying forecast uncertainty has become important to improve the quality of forecasts and decision making. In the natural gas industry, the task of the gas controllers is to guide the hourly natural gas flow in such a way that it remains within a certain daily maximum and minimum flow limits to avoid penalties. Due to inherent uncertainties in the natural gas forecasts, setting such maximum and minimum flow limits a day or more in advance is difficult. Probabilistic forecasts (cumulative distribution functions), which quantify forecast uncertainty, are a useful tool to guide gas controllers to make such tough decisions. Three methods (parametric, semi-parametric, and non-parametric) are presented in this dissertation to generate 168-hour horizon probabilistic forecasts for two real utilities (electricity and natural gas) in the US. Probabilistic forecasting is used as a tool to solve a real-life problem in the natural gas industry. A benchmark was created based on the existing solution, which assumes forecast error is normal. Two new probabilistic forecasting methods are implemented in this work without the normality assumption. There is no single popular evaluation technique available to assess probabilistic forecasts, which is one reason for people’s lack of interest in using probabilistic forecasts. Existing scoring rules are complicated, dataset dependent, and provide less emphasis on reliability (empirical distribution matches with observed distribution) than sharpness (the smallest distance between any two quantiles of a CDF). A graphical way to evaluate probabilistic forecasts along with two new scoring rules are offered in this work. The non-parametric and semi-parametric probabilistic forecasting methods outperformed the benchmark method during unusual days (difficult days to forecast) as well as on other days

Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations

The increased digitalisation and monitoring of the energy system opens up numerous opportunities to decarbonise the energy system. Applications on low voltage, local networks, such as community energy markets and smart storage will facilitate decarbonisation, but they will require advanced control and management. Reliable forecasting will be a necessary component of many of these systems to anticipate key features and uncertainties. Despite this urgent need, there has not yet been an extensive investigation into the current state-of-the-art of low voltage level forecasts, other than at the smart meter level. This paper aims to provide a comprehensive overview of the landscape, current approaches, core applications, challenges and recommendations. Another aim of this paper is to facilitate the continued improvement and advancement in this area. To this end, the paper also surveys some of the most relevant and promising trends. It establishes an open, community-driven list of the known low voltage level open datasets to encourage further research and development.Comment: 37 pages, 6 figures, 2 tables, review pape