# Forecasting Electricity Prices: A Machine Learning Approach
## 2 Challenges of Electricity Price Forecasting  
Since the deregulation in electricity markets, price forecasting has become an extremely valuable tool. Electricity market participants make extensive use of price prediction techniques; an accurate price forecast for an electricity market has a decisive impact on the bidding strategies by producers or consumers and on the price negotiations of a bilateral contract. In both pool markets and bilateral contracts, predicting next-day electricity prices or prices for the next several months (or even years) is of the foremost importance to allow electric companies to adjust their daily bids or monthly/yearly schedules for contracts. As energy service companies buy electricity from the pool and from bilateral contracts to sell it to their clients, they seek good short-term and long-term price forecast information to maximize their own benefits [3]. As [PERSON], [PERSON] [24] argues, electricity prices, and particularly price spikes, are influenced heavily by a wide range of factors (e.g., transmission congestion, generation outages, and market participant behaviors) other than the electricity demand. These factors, their hidden relationships, and the uncertainties associated with them are difficult to incorporate into electricity price forecasting models. Artificial intelligence-based electricity price forecasting methods have been recently proposed to counter the above-mentioned problems [18; 21; 25]. The main reasoning for employing these methods is their ability to infer hidden relationships in data. However, many of these methods still have along several shortcomings that limit the extent of their exploitation [3]. These deficiencies include high prediction errors for price forecasting, the inability to effectively exploit local data characteristics or capture global data trends, and the excessive computational requirements for price prediction. Overall, despite the current efforts, significant errors remain in price predictions, confirming the need for more efficient forecast methods in this field [6]. To answer this call, this work proposes a machine learning system based on a recently defined variant of genetic programming, a machine learning technique that has demonstrated its ability to produce human-competitive results [26]. In particular, as discussed in the following section, the proposed system provides energy market participants a viable option for addressing the energy price forecasting task considering a 24 hour prediction horizon, thereby overcoming the main limitations of existing methods. Among the vast amount of existing machine learning techniques that are presently available, this study focuses on genetic programming for several reasons. First of all, there is increasing interest in the scientific literature concerning the interpretability of artificial intelligence (AI) models [27]. This interest ranges from the business domain [28] to the health-care sector [29]. Moreover, AI has been identified to potentially offer significant benefits to a large range of domains, including the energy market [30]. The growing interest in this topic is accompanied by the popularity of AI-based models: Despite their ability to produce human-competitive results in an increasing number of complex tasks, these models are essentially black boxes, as no information is provided about how they achieved their predictions. In other words, as a final AI user, we know the prediction that a given AI model produced, but we do not know why this particular prediction was made. This could be an important limitation to the more wide-scale adoption of AI, as users show more willingness to use a particular model when they can understand why particular decisions are being made [31]. In the context of the applicative domain considered in this project, the possibility of obtaining explainable models could allow us to make better-informed decisions on how operate in the energy market.