Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases

Evaluation of capacity expansion scenarios for the Hellenic electric sector

During the last years, fossil fuels covered the largest share of the produced electricity of the Greek electric sector. In the near term, many units’ decommissioning is expected, a fact that provides the landscape for more investments in gas fired plants and renewable energy resources. The present study analyzes the current state of the Greek energy system, focusing on the historical evolution of electricity consumption and electricity generation. Next, scenarios of future power system expansion that differ in the composition of power generation are examined. The scenarios are compared to the baseline scenario of the 10-year transmission system development plan issued by Independent Power Transmission Operator (IPTO) SA. The objective is to examine the impact of various power generation technologies on the capacity expansion costs and the emission of environmental pollutants and to provide a detailed study for interested parties concerning the expected benefits and limitations of increased penetration of various sources on the power system. © 2021 Elsevier Inc. All rights reserved

Retailer profit maximization with the assistance of price and load forecasting processes

The liberalization of electricity markets from structured monopolies to competitive forms provided the motives for more market participants to get involved and operate in day-ahead markets. Among them, the retailers act as the intermediates between generation companies and consumers. The retailer procures electricity mainly from pool market and forward contracts. The scope is to maximize its profits through the solution of a profit maximization problem. The system marginal prices are assumed a stochastic variable. A poor prediction would eventually lead to decreases in revenues. Instead of treating the system price as a stochastic variable where a set of scenarios are formulated, a computational intelligence based forecasting system can be implemented in order to decrease the optimization problem complexity. Also, another source of stochasticity are the consumers load patterns. In this case, a short-term load forecasting system can aid on the strategic decision of the retailer such as the amount of the electricity procurement and tariff structure. The present study presents a profit maximization method for retailers in deregulated markets. Two crucial variables, i.e., price and load, are not simulated via stochastic programming; instead of this, accurate forecasting algorithms are implemented to provide better predictions. © 2021 Elsevier Inc. All rights reserved

A Metaheuristics-Based Inputs Selection and Training Set Formation Method for Load Forecasting

Load forecasting is a procedure of fundamental importance in power systems operation and planning. Many entities can benefit from accurate load forecasting such as generation companies, systems operators, retailers, prosumers, and others. A variety of models have been proposed so far in the literature. Among them, artificial neural networks are a favorable approach mainly due to their potential for capturing the relationship between load and other parameters. The forecasting performance highly depends on the number and types of inputs. The present paper presents a particle swarm optimization (PSO) two-step method for increasing the performance of short-term load forecasting (STLF). During the first step, PSO is applied to derive the optimal types of inputs for a neural network. Next, PSO is applied again so that the available training data is split into homogeneous clusters. For each cluster, a different neural network is utilized. Experimental results verify the robustness of the proposed approach in a bus load forecasting problem. Also, the proposed algorithm is checked on a load profiling problem where it outperforms the most common algorithms of the load profiling-related literature. During input selection, the weights update is held in asymmetrical duration. The weights of the training phase require more time compared with the test phase. © 2022 by the authors

Renewable energy sources generation forecasting in aggregated energy system level

Renewable Energy Sources (RES) generation forecasting is an approach to handle the stochasticity of RES. This concept is very crucial to transform RES plants into dispatchable and integrated them for contemporary energy markets. The majority of the literature focuses on individual plants. The data are collected in a site and used as inputs in the forecasting model. The present paper is centered on aggregated energy system level. The total capacities of Photovoltaics (PV) and Wind Turbine (WT) power of a country are regarded. A scenarios-based approach is followed in order to investigate how the number and types of inputs influence the forecasting performance. While most studies of the literature focus on individual systems, the paper contributes on the RES forecasting literature through the consideration of the total PV and WT generation capacity on aggregated power system level. © 2021 IEE

Smart home energy management processes support through machine learning algorithms

Smart Home Energy Management Systems can manifest energy consumption reduction targets in the residential sector and can be viewed as an approach to transform the consumer into an active prosumer. The present paper presents a smart home energy management system that includes flexible appliances, electric vehicles, and energy storage units. Efficient forecasting algorithms support the robust operation of the smart home energy management system. Specifically, the smart home energy management system receives as inputs forecasts of demand, renewable energy sources including photovoltaics and Wind Turbine generations, and real-time prices. In order to minimize energy costs, a variety of algorithms is compared to provide highly accurate forecasts. © 2022 The Author(s

Utilizing Harmonics in Sequential and Parallel Disaggregation Schemes

Scope of this paper is to examine the performance of two disaggregation schemes for NILM algorithms based on the utilization of current harmonic vectors. The analysis is based on measurements performed on real residential installation and the utilized features regarding the formulation of the load signatures of the appliances refer to the odd harmonic current vectors up to the fifth harmonic order. Moreover, a large number of measured appliance combinations is used for the application of the disaggregation schemes in order to evaluate their performance. The first scheme relies on sequential disaggregation for the harmonic orders, starting from the fifth and moving on to the first. The second scheme concerns simultaneous disaggregation of all harmonic orders in a parallel manner. The merits and flaws of both schemes are highlighted and the results indicate that for the parallel disaggregation scheme the appliance identification performance is higher at the expense of the computational burden. © 2019 IEEE

A Machine Learning Approach for NILM based on Odd Harmonic Current Vectors

This paper examines the application of machine learning techniques in NILM methodologies based on the first three odd harmonic order current vectors as the only attributes of the appliances. Proper formulation of the measured current waveform of appliances' combinations is also presented. We apply our methodology on performed measurements of typical Low Voltage residential installations considering harmonic order currents as the input features for both the training and disaggregation scheme. Our results support the hypothesis that the identification performance is enhanced when higher harmonic currents are included in the NILM methodology. © 2019 IEEE